{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Xornotor/PPGEEC-CompEvolutiva-Atividades/blob/main/1C_Computa%C3%A7%C3%A3o_Evolutiva_e_Meta_heur%C3%ADsticas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Avaliação 1C - Computação Evolutiva e Meta-heurísticas**\n",
        "\n",
        "**Docente:** Prof. Dr. Edmar Egídio Purcino de Souza\n",
        "\n",
        "**Discentes:** André Paiva, Gabriel Lucas, Márcio Barros e Shaísta Câmara"
      ],
      "metadata": {
        "id": "s6PNZN8nDmml"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from numpy import arange\n",
        "from numpy import exp\n",
        "from numpy import sqrt\n",
        "from numpy import cos\n",
        "from numpy import e\n",
        "from numpy import pi\n",
        "from numpy import meshgrid"
      ],
      "metadata": {
        "id": "d5-XdXuTH-hM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the Rastrigin function\n",
        "def levi(chromosome):\n",
        "    x1, x2 = chromosome\n",
        "    term1 = np.sin(3 * np.pi * x1)**2\n",
        "    term2 = (x1 - 1)**2 * (1 + np.sin(3 * np.pi * x2)**2)\n",
        "    term3 = (x2 - 1)**2 * (1 + np.sin(2 * np.pi * x2)**2)\n",
        "    return term1 + term2 + term3\n",
        "\n",
        "def drop_wave(v):\n",
        "    x, y = v\n",
        "    numerator = 1 + np.cos(12 * np.sqrt(x**2 + y**2))\n",
        "    denominator = 0.5 * (x**2 + y**2) + 2\n",
        "\n",
        "    # Avoid division by zero\n",
        "    # Change: Check if any element in the denominator array is zero\n",
        "    if np.any(denominator == 0):\n",
        "        # If any element is zero, replace it with a small value to avoid division by zero\n",
        "        denominator[denominator == 0] = 1e-10 # or any other appropriate value\n",
        "\n",
        "    return -numerator / denominator"
      ],
      "metadata": {
        "id": "ywQfbZCvIAMf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the PSO algorithm\n",
        "\n",
        "def pso(cost_func, dim=2, num_particles=30, max_iter=500, w=0.5, c1=1, c2=2,max_patience=50, logprint=False):\n",
        "    # Initialize particles and velocities\n",
        "    particles = np.random.uniform(-5.12, 5.12, (num_particles, dim))\n",
        "    velocities = np.zeros((num_particles, dim))\n",
        "\n",
        "    # Initialize the best positions and fitness values\n",
        "    best_positions = np.copy(particles)\n",
        "    best_fitness = np.array([cost_func(p) for p in particles])\n",
        "    swarm_best_position = best_positions[np.argmin(best_fitness)]\n",
        "    swarm_best_fitness = np.min(best_fitness)\n",
        "    history = []\n",
        "    patience = 0\n",
        "    # Iterate through the specified number of iterations, updating the velocity and position of each particle at each iteration\n",
        "    for i in range(max_iter):\n",
        "        # Update velocities\n",
        "        r1 = np.random.uniform(0, 1, (num_particles, dim))\n",
        "        r2 = np.random.uniform(0, 1, (num_particles, dim))\n",
        "        velocities = w * velocities + c1 * r1 * (best_positions - particles) + c2 * r2 * (swarm_best_position - particles)\n",
        "\n",
        "        # Update positions\n",
        "        particles += velocities\n",
        "\n",
        "        # Evaluate fitness of each particle\n",
        "        fitness_values = np.array([cost_func(p) for p in particles])\n",
        "\n",
        "        # Update best positions and fitness values\n",
        "        improved_indices = np.where(fitness_values < best_fitness)\n",
        "        best_positions[improved_indices] = particles[improved_indices]\n",
        "        best_fitness[improved_indices] = fitness_values[improved_indices]\n",
        "        if np.min(fitness_values) < swarm_best_fitness:\n",
        "            swarm_best_position = particles[np.argmin(fitness_values)]\n",
        "            swarm_best_fitness = np.min(fitness_values)\n",
        "            patience = 0\n",
        "        else:\n",
        "            patience += 1\n",
        "\n",
        "        if patience == max_patience:\n",
        "            break\n",
        "\n",
        "        history.append(swarm_best_fitness)\n",
        "        if logprint:\n",
        "            print(f\"Iteration {i+1}: Best fitness = {swarm_best_fitness}\")\n",
        "\n",
        "    # Return the best solution found by the PSO algorithm\n",
        "    return swarm_best_position, swarm_best_fitness, history"
      ],
      "metadata": {
        "id": "0ADor90DIDJ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dimensions of the problem\n",
        "dim = 2\n",
        "objective = rastrigin\n",
        "solution, fitness, history = pso(objective, dim=dim, logprint=True)\n",
        "\n",
        "# Print the solution and fitness value\n",
        "print('Solution:', solution)\n",
        "print('Fitness:', fitness)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zu8ESZAxITtQ",
        "outputId": "1ba8d9b9-ca2b-403a-8dd7-a163abb304a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration 1: Best fitness = 2.730509486272659\n",
            "Iteration 2: Best fitness = 2.730509486272659\n",
            "Iteration 3: Best fitness = 2.7220430414347447\n",
            "Iteration 4: Best fitness = 2.7220430414347447\n",
            "Iteration 5: Best fitness = 2.7220430414347447\n",
            "Iteration 6: Best fitness = 2.7220430414347447\n",
            "Iteration 7: Best fitness = 1.5226211271091188\n",
            "Iteration 8: Best fitness = 1.1897630533165113\n",
            "Iteration 9: Best fitness = 1.0629658383760905\n",
            "Iteration 10: Best fitness = 1.0629658383760905\n",
            "Iteration 11: Best fitness = 1.0629658383760905\n",
            "Iteration 12: Best fitness = 1.0629658383760905\n",
            "Iteration 13: Best fitness = 1.0028883738468615\n",
            "Iteration 14: Best fitness = 0.35782957584174113\n",
            "Iteration 15: Best fitness = 0.35782957584174113\n",
            "Iteration 16: Best fitness = 0.35782957584174113\n",
            "Iteration 17: Best fitness = 0.35782957584174113\n",
            "Iteration 18: Best fitness = 0.35782957584174113\n",
            "Iteration 19: Best fitness = 0.35782957584174113\n",
            "Iteration 20: Best fitness = 0.35782957584174113\n",
            "Iteration 21: Best fitness = 0.03242845402099448\n",
            "Iteration 22: Best fitness = 0.03242845402099448\n",
            "Iteration 23: Best fitness = 0.03242845402099448\n",
            "Iteration 24: Best fitness = 0.03242845402099448\n",
            "Iteration 25: Best fitness = 0.03242845402099448\n",
            "Iteration 26: Best fitness = 0.03242845402099448\n",
            "Iteration 27: Best fitness = 0.015590179476987487\n",
            "Iteration 28: Best fitness = 0.01514765861757894\n",
            "Iteration 29: Best fitness = 0.0011136018676083381\n",
            "Iteration 30: Best fitness = 0.0011136018676083381\n",
            "Iteration 31: Best fitness = 0.0011136018676083381\n",
            "Iteration 32: Best fitness = 0.0008607340719493095\n",
            "Iteration 33: Best fitness = 0.0008607340719493095\n",
            "Iteration 34: Best fitness = 0.0006990172440595188\n",
            "Iteration 35: Best fitness = 0.0003361907668519848\n",
            "Iteration 36: Best fitness = 0.0003361907668519848\n",
            "Iteration 37: Best fitness = 0.0003361907668519848\n",
            "Iteration 38: Best fitness = 0.0003361907668519848\n",
            "Iteration 39: Best fitness = 0.00016636985156992523\n",
            "Iteration 40: Best fitness = 0.00016636985156992523\n",
            "Iteration 41: Best fitness = 0.00016636985156992523\n",
            "Iteration 42: Best fitness = 0.00016636985156992523\n",
            "Iteration 43: Best fitness = 0.00010250286727853108\n",
            "Iteration 44: Best fitness = 0.00010250286727853108\n",
            "Iteration 45: Best fitness = 2.0934620508228363e-06\n",
            "Iteration 46: Best fitness = 2.0934620508228363e-06\n",
            "Iteration 47: Best fitness = 2.0934620508228363e-06\n",
            "Iteration 48: Best fitness = 2.0934620508228363e-06\n",
            "Iteration 49: Best fitness = 2.0934620508228363e-06\n",
            "Iteration 50: Best fitness = 1.152841150542372e-06\n",
            "Iteration 51: Best fitness = 1.152841150542372e-06\n",
            "Iteration 52: Best fitness = 1.152841150542372e-06\n",
            "Iteration 53: Best fitness = 1.152841150542372e-06\n",
            "Iteration 54: Best fitness = 1.152841150542372e-06\n",
            "Iteration 55: Best fitness = 6.46362146028423e-07\n",
            "Iteration 56: Best fitness = 6.46362146028423e-07\n",
            "Iteration 57: Best fitness = 6.46362146028423e-07\n",
            "Iteration 58: Best fitness = 6.46362146028423e-07\n",
            "Iteration 59: Best fitness = 1.0665504746043553e-07\n",
            "Iteration 60: Best fitness = 1.0665504746043553e-07\n",
            "Iteration 61: Best fitness = 1.0665504746043553e-07\n",
            "Iteration 62: Best fitness = 1.0665504746043553e-07\n",
            "Iteration 63: Best fitness = 1.0665504746043553e-07\n",
            "Iteration 64: Best fitness = 3.66894390424477e-08\n",
            "Iteration 65: Best fitness = 3.66894390424477e-08\n",
            "Iteration 66: Best fitness = 3.66894390424477e-08\n",
            "Iteration 67: Best fitness = 3.66894390424477e-08\n",
            "Iteration 68: Best fitness = 2.829286671612863e-08\n",
            "Iteration 69: Best fitness = 4.3468233457133465e-09\n",
            "Iteration 70: Best fitness = 2.3961490569490707e-09\n",
            "Iteration 71: Best fitness = 1.140755045980768e-09\n",
            "Iteration 72: Best fitness = 8.127045703076874e-10\n",
            "Iteration 73: Best fitness = 1.439772745470691e-10\n",
            "Iteration 74: Best fitness = 1.439772745470691e-10\n",
            "Iteration 75: Best fitness = 1.439772745470691e-10\n",
            "Iteration 76: Best fitness = 4.32791580351477e-11\n",
            "Iteration 77: Best fitness = 3.1519675758318044e-11\n",
            "Iteration 78: Best fitness = 6.842526545369765e-12\n",
            "Iteration 79: Best fitness = 3.232969447708456e-12\n",
            "Iteration 80: Best fitness = 1.886490963443066e-12\n",
            "Iteration 81: Best fitness = 1.4495071809506044e-12\n",
            "Iteration 82: Best fitness = 1.4495071809506044e-12\n",
            "Iteration 83: Best fitness = 1.4495071809506044e-12\n",
            "Iteration 84: Best fitness = 9.734435479913373e-13\n",
            "Iteration 85: Best fitness = 9.734435479913373e-13\n",
            "Iteration 86: Best fitness = 9.734435479913373e-13\n",
            "Iteration 87: Best fitness = 9.734435479913373e-13\n",
            "Iteration 88: Best fitness = 1.6342482922482304e-13\n",
            "Iteration 89: Best fitness = 1.6342482922482304e-13\n",
            "Iteration 90: Best fitness = 1.6342482922482304e-13\n",
            "Iteration 91: Best fitness = 1.6342482922482304e-13\n",
            "Iteration 92: Best fitness = 1.1723955140041653e-13\n",
            "Iteration 93: Best fitness = 1.1723955140041653e-13\n",
            "Iteration 94: Best fitness = 1.1723955140041653e-13\n",
            "Iteration 95: Best fitness = 2.4868995751603507e-14\n",
            "Iteration 96: Best fitness = 2.4868995751603507e-14\n",
            "Iteration 97: Best fitness = 2.4868995751603507e-14\n",
            "Iteration 98: Best fitness = 2.4868995751603507e-14\n",
            "Iteration 99: Best fitness = 2.4868995751603507e-14\n",
            "Iteration 100: Best fitness = 3.552713678800501e-15\n",
            "Iteration 101: Best fitness = 3.552713678800501e-15\n",
            "Iteration 102: Best fitness = 3.552713678800501e-15\n",
            "Iteration 103: Best fitness = 3.552713678800501e-15\n",
            "Iteration 104: Best fitness = 3.552713678800501e-15\n",
            "Iteration 105: Best fitness = 3.552713678800501e-15\n",
            "Iteration 106: Best fitness = 3.552713678800501e-15\n",
            "Iteration 107: Best fitness = 0.0\n",
            "Iteration 108: Best fitness = 0.0\n",
            "Iteration 109: Best fitness = 0.0\n",
            "Iteration 110: Best fitness = 0.0\n",
            "Iteration 111: Best fitness = 0.0\n",
            "Iteration 112: Best fitness = 0.0\n",
            "Iteration 113: Best fitness = 0.0\n",
            "Iteration 114: Best fitness = 0.0\n",
            "Iteration 115: Best fitness = 0.0\n",
            "Iteration 116: Best fitness = 0.0\n",
            "Iteration 117: Best fitness = 0.0\n",
            "Iteration 118: Best fitness = 0.0\n",
            "Iteration 119: Best fitness = 0.0\n",
            "Iteration 120: Best fitness = 0.0\n",
            "Iteration 121: Best fitness = 0.0\n",
            "Iteration 122: Best fitness = 0.0\n",
            "Iteration 123: Best fitness = 0.0\n",
            "Iteration 124: Best fitness = 0.0\n",
            "Iteration 125: Best fitness = 0.0\n",
            "Iteration 126: Best fitness = 0.0\n",
            "Iteration 127: Best fitness = 0.0\n",
            "Iteration 128: Best fitness = 0.0\n",
            "Iteration 129: Best fitness = 0.0\n",
            "Iteration 130: Best fitness = 0.0\n",
            "Iteration 131: Best fitness = 0.0\n",
            "Iteration 132: Best fitness = 0.0\n",
            "Iteration 133: Best fitness = 0.0\n",
            "Iteration 134: Best fitness = 0.0\n",
            "Iteration 135: Best fitness = 0.0\n",
            "Iteration 136: Best fitness = 0.0\n",
            "Iteration 137: Best fitness = 0.0\n",
            "Iteration 138: Best fitness = 0.0\n",
            "Iteration 139: Best fitness = 0.0\n",
            "Iteration 140: Best fitness = 0.0\n",
            "Iteration 141: Best fitness = 0.0\n",
            "Iteration 142: Best fitness = 0.0\n",
            "Iteration 143: Best fitness = 0.0\n",
            "Iteration 144: Best fitness = 0.0\n",
            "Iteration 145: Best fitness = 0.0\n",
            "Iteration 146: Best fitness = 0.0\n",
            "Iteration 147: Best fitness = 0.0\n",
            "Iteration 148: Best fitness = 0.0\n",
            "Iteration 149: Best fitness = 0.0\n",
            "Iteration 150: Best fitness = 0.0\n",
            "Iteration 151: Best fitness = 0.0\n",
            "Iteration 152: Best fitness = 0.0\n",
            "Iteration 153: Best fitness = 0.0\n",
            "Iteration 154: Best fitness = 0.0\n",
            "Iteration 155: Best fitness = 0.0\n",
            "Iteration 156: Best fitness = 0.0\n",
            "Solution: [3.43806288e-09 1.35044865e-09]\n",
            "Fitness: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a meshgrid for visualization\n",
        "# sample input range uniformly at 0.1 increments\n",
        "x = arange(-5, 5, 0.1)\n",
        "y = arange(-5, 5, 0.1)\n",
        "\n",
        "# x = np.linspace(-5.12, 5.12, 100)\n",
        "# y = np.linspace(-5.12, 5.12, 100)\n",
        "X, Y = np.meshgrid(x, y)\n",
        "Z = objective([X, Y])\n",
        "\n",
        "# Create a 3D plot of the Rastrigin function\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "ax.plot_surface(X, Y, Z, cmap='viridis')\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('y')\n",
        "ax.set_zlabel('z')\n",
        "\n",
        "# Plot the solution found by the PSO algorithm\n",
        "ax.scatter(solution[0], solution[1], fitness, color='red')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "67hWFr7fIF4G",
        "outputId": "35c92156-2611-4bd4-8a87-ebd96a32dc4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I4F1fiTwKihi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the optimization process\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history, marker='o')\n",
        "plt.title('Optimization Using PSO')\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('Objective Function Value')\n",
        "plt.grid(False)  # Turn off gridlines\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "EHgdhQoZIGHb",
        "outputId": "fa5d5514-93b0-4a2a-c52e-242ad035e85d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experimento 1: Número Fixo de Iterações e Inicializações (Drop-Wave)."
      ],
      "metadata": {
        "id": "JAPJVLfy0w81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dimensions of the problem\n",
        "exp1drop_data = []\n",
        "INIT = 100\n",
        "dim = 2\n",
        "objective = drop_wave\n",
        "for i in range(INIT):\n",
        "  print(f\"\\rInicialização {i}...\", end='')\n",
        "  solution, fitness, _ = pso(objective, dim=dim)\n",
        "  solution = np.append(solution, fitness)\n",
        "  exp1drop_data.append(solution)\n",
        "exp1drop_data = np.array(exp1drop_data)\n",
        "exp1drop_df = pd.DataFrame(exp1drop_data, columns=['X1', 'X2', 'Fitness'])\n",
        "exp1drop_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "qpPpnVH01TCS",
        "outputId": "f0d145e9-d128-410a-dfb3-c6ee63a4aa8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inicialização 99..."
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              X1            X2  Fitness\n",
              "0  -6.072598e-10 -7.900111e-10     -1.0\n",
              "1   1.363710e-09 -4.846950e-10     -1.0\n",
              "2  -8.745257e-10  1.145183e-09     -1.0\n",
              "3  -6.464832e-10 -1.114764e-09     -1.0\n",
              "4   1.197211e-09 -5.885479e-10     -1.0\n",
              "..           ...           ...      ...\n",
              "95  1.065611e-10 -1.028288e-10     -1.0\n",
              "96  9.719198e-10 -3.118927e-10     -1.0\n",
              "97 -1.252463e-09  6.941320e-11     -1.0\n",
              "98  9.360917e-10 -2.011333e-10     -1.0\n",
              "99 -1.559116e-10  1.211751e-09     -1.0\n",
              "\n",
              "[100 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d1e81e0-3b70-4dcf-a660-f4cba25b59ae\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Fitness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-6.072598e-10</td>\n",
              "      <td>-7.900111e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.363710e-09</td>\n",
              "      <td>-4.846950e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-8.745257e-10</td>\n",
              "      <td>1.145183e-09</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-6.464832e-10</td>\n",
              "      <td>-1.114764e-09</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.197211e-09</td>\n",
              "      <td>-5.885479e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1.065611e-10</td>\n",
              "      <td>-1.028288e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>9.719198e-10</td>\n",
              "      <td>-3.118927e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>-1.252463e-09</td>\n",
              "      <td>6.941320e-11</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>9.360917e-10</td>\n",
              "      <td>-2.011333e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>-1.559116e-10</td>\n",
              "      <td>1.211751e-09</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d1e81e0-3b70-4dcf-a660-f4cba25b59ae')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2d1e81e0-3b70-4dcf-a660-f4cba25b59ae button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2d1e81e0-3b70-4dcf-a660-f4cba25b59ae');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-be985193-152d-4f58-94fc-45e8ed9b9e3f\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be985193-152d-4f58-94fc-45e8ed9b9e3f')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-be985193-152d-4f58-94fc-45e8ed9b9e3f button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_7cf0507d-5564-47db-9a39-a5d8f5bc29ff\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp1_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7cf0507d-5564-47db-9a39-a5d8f5bc29ff button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp1_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp1_df",
              "summary": "{\n  \"name\": \"exp1_df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2111093665810288,\n        \"min\": -0.47952664089547276,\n        \"max\": 0.5187016109071885,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          -1.3022226591698382e-09,\n          0.38059470124493533,\n          0.21415050690888737\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19062068150111347,\n        \"min\": -0.5159646167798548,\n        \"max\": 0.5202152798920308,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          -3.219917050559794e-10,\n          0.35464334375367346,\n          0.47409259478470805\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029363246241258415,\n        \"min\": -1.0,\n        \"max\": -0.9362453278079417,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1.0,\n          -0.9362453278079418,\n          -0.9362453278079417\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "sns.histplot(exp1drop_df, x='Fitness', bins=30, ax=ax)\n",
        "ax.set_title(\"Experimento 1: Dispersão (Drop-Wave).\")\n",
        "ax.set_ylabel(\"Contagem\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "jvK_EMku5YkW",
        "outputId": "0120e426-bbf4-4832-e86a-ae9c6625ba8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experimento 1: Número Fixo de Iterações e Inicializações (Levi)."
      ],
      "metadata": {
        "id": "UM0kmlT27551"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dimensions of the problem\n",
        "exp1levi_data = []\n",
        "INIT = 100\n",
        "dim = 2\n",
        "objective = levi\n",
        "for i in range(INIT):\n",
        "  print(f\"\\rInicialização {i}...\", end='')\n",
        "  solution, fitness, _ = pso(objective, dim=dim)\n",
        "  solution = np.append(solution, fitness)\n",
        "  exp1levi_data.append(solution)\n",
        "exp1levi_data = np.array(exp1levi_data)\n",
        "exp1levi_df = pd.DataFrame(exp1levi_data, columns=['X1', 'X2', 'Fitness'])\n",
        "exp1levi_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "d503b9a1-6037-4a4d-fe5f-0b95db4df7a6",
        "id": "4GDCietU7552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inicialização 99..."
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     X1   X2       Fitness\n",
              "0   1.0  1.0  1.349784e-31\n",
              "1   1.0  1.0  1.349784e-31\n",
              "2   1.0  1.0  1.349784e-31\n",
              "3   1.0  1.0  1.349784e-31\n",
              "4   1.0  1.0  1.349784e-31\n",
              "..  ...  ...           ...\n",
              "95  1.0  1.0  1.349784e-31\n",
              "96  1.0  1.0  1.349784e-31\n",
              "97  1.0  1.0  1.349784e-31\n",
              "98  1.0  1.0  1.349784e-31\n",
              "99  1.0  1.0  1.349784e-31\n",
              "\n",
              "[100 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f5b52ab0-cc2d-4cc4-aa72-b08e792969ec\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Fitness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.349784e-31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5b52ab0-cc2d-4cc4-aa72-b08e792969ec')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f5b52ab0-cc2d-4cc4-aa72-b08e792969ec button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f5b52ab0-cc2d-4cc4-aa72-b08e792969ec');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-71f66947-d147-4e7c-be82-8d13d37b44c6\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-71f66947-d147-4e7c-be82-8d13d37b44c6')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-71f66947-d147-4e7c-be82-8d13d37b44c6 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_bac57a95-c705-4577-874a-9f582d568261\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp2_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bac57a95-c705-4577-874a-9f582d568261 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp2_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp2_df",
              "summary": "{\n  \"name\": \"exp2_df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6406711508333185e-46,\n        \"min\": 1.3497838043956716e-31,\n        \"max\": 1.3497838043956716e-31,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.3497838043956716e-31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "sns.histplot(exp1levi_df, x='Fitness', bins=30, ax=ax)\n",
        "ax.set_title(\"Experimento 1: Dispersão (Levi).\")\n",
        "ax.set_ylabel(\"Contagem\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "c3381b60-314e-4805-9949-903a28b67ab1",
        "id": "d9j9VIgl87Jm"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dimensions of the problem\n",
        "exp1_data = []\n",
        "INIT = 100\n",
        "dim = 2\n",
        "objective = drop_wave\n",
        "for i in range(INIT):\n",
        "  print(f\"\\rInicialização {i}...\", end='')\n",
        "  solution, fitness, _ = pso(objective, dim=dim)\n",
        "  solution = np.append(solution, fitness)\n",
        "  exp1_data.append(solution)\n",
        "exp1_data = np.array(exp1_data)\n",
        "exp1_df = pd.DataFrame(exp1_data, columns=['X1', 'X2', 'Fitness'])\n",
        "exp1_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "f0d145e9-d128-410a-dfb3-c6ee63a4aa8e",
        "id": "G-Nhe8879j7z"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inicialização 99..."
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              X1            X2  Fitness\n",
              "0  -6.072598e-10 -7.900111e-10     -1.0\n",
              "1   1.363710e-09 -4.846950e-10     -1.0\n",
              "2  -8.745257e-10  1.145183e-09     -1.0\n",
              "3  -6.464832e-10 -1.114764e-09     -1.0\n",
              "4   1.197211e-09 -5.885479e-10     -1.0\n",
              "..           ...           ...      ...\n",
              "95  1.065611e-10 -1.028288e-10     -1.0\n",
              "96  9.719198e-10 -3.118927e-10     -1.0\n",
              "97 -1.252463e-09  6.941320e-11     -1.0\n",
              "98  9.360917e-10 -2.011333e-10     -1.0\n",
              "99 -1.559116e-10  1.211751e-09     -1.0\n",
              "\n",
              "[100 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d1e81e0-3b70-4dcf-a660-f4cba25b59ae\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Fitness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-6.072598e-10</td>\n",
              "      <td>-7.900111e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.363710e-09</td>\n",
              "      <td>-4.846950e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-8.745257e-10</td>\n",
              "      <td>1.145183e-09</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-6.464832e-10</td>\n",
              "      <td>-1.114764e-09</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.197211e-09</td>\n",
              "      <td>-5.885479e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1.065611e-10</td>\n",
              "      <td>-1.028288e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>9.719198e-10</td>\n",
              "      <td>-3.118927e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>-1.252463e-09</td>\n",
              "      <td>6.941320e-11</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>9.360917e-10</td>\n",
              "      <td>-2.011333e-10</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>-1.559116e-10</td>\n",
              "      <td>1.211751e-09</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d1e81e0-3b70-4dcf-a660-f4cba25b59ae')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2d1e81e0-3b70-4dcf-a660-f4cba25b59ae button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2d1e81e0-3b70-4dcf-a660-f4cba25b59ae');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-be985193-152d-4f58-94fc-45e8ed9b9e3f\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be985193-152d-4f58-94fc-45e8ed9b9e3f')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-be985193-152d-4f58-94fc-45e8ed9b9e3f button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_7cf0507d-5564-47db-9a39-a5d8f5bc29ff\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp1_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7cf0507d-5564-47db-9a39-a5d8f5bc29ff button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp1_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp1_df",
              "summary": "{\n  \"name\": \"exp1_df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2111093665810288,\n        \"min\": -0.47952664089547276,\n        \"max\": 0.5187016109071885,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          -1.3022226591698382e-09,\n          0.38059470124493533,\n          0.21415050690888737\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19062068150111347,\n        \"min\": -0.5159646167798548,\n        \"max\": 0.5202152798920308,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          -3.219917050559794e-10,\n          0.35464334375367346,\n          0.47409259478470805\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029363246241258415,\n        \"min\": -1.0,\n        \"max\": -0.9362453278079417,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1.0,\n          -0.9362453278079418,\n          -0.9362453278079417\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "sns.histplot(exp1_df, x='Fitness', bins=30, ax=ax)\n",
        "ax.set_title(\"Experimento 1: Dispersão (Drop-Wave).\")\n",
        "ax.set_ylabel(\"Contagem\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "0120e426-bbf4-4832-e86a-ae9c6625ba8f",
        "id": "UdJOY0K_9j70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experimento 2: Excursão do Número de Máximas Iterações (Drop-Wave)."
      ],
      "metadata": {
        "id": "uKKaBNWl94dD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dimensions of the problem\n",
        "exp2drop_data = []\n",
        "INIT = 100\n",
        "dim = 2\n",
        "objective = drop_wave\n",
        "for max_iter in range(10, 101, 10):\n",
        "  for i in range(INIT):\n",
        "    print(f\"\\rMax.iter. {max_iter}, Inicialização {i}...\", end='')\n",
        "    solution, fitness, _ = pso(objective, dim=dim, max_iter=max_iter, w=0.7) #Mudamos o W\n",
        "    solution = np.append(solution, fitness)\n",
        "    solution = np.append(solution, max_iter)\n",
        "    exp2drop_data.append(solution)\n",
        "exp2drop_data = np.array(exp2drop_data)\n",
        "exp2drop_df = pd.DataFrame(exp2drop_data, columns=['X1', 'X2', 'Fitness', 'Máximas Iterações'])\n",
        "exp2drop_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "28584c44-be19-46d9-898e-9a33cb9def47",
        "id": "QmvtlIry94dE"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max.iter. 100, Inicialização 99..."
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           X1        X2   Fitness  Máximas Iterações\n",
              "0    0.520745 -0.141423 -0.935617               10.0\n",
              "1    6.048559 -0.548054 -0.935474               10.0\n",
              "2    1.612844  0.032884 -0.935686               10.0\n",
              "3   -0.511647  0.088807 -0.936217               10.0\n",
              "4    0.390923  0.790109 -0.930908               10.0\n",
              "..        ...       ...       ...                ...\n",
              "995 -0.000005 -0.000989 -0.999965              100.0\n",
              "996 -0.000020 -0.000009 -1.000000              100.0\n",
              "997  0.014401 -0.005306 -0.998797              100.0\n",
              "998  0.000398 -0.000096 -0.999999              100.0\n",
              "999 -0.000008  0.000006 -1.000000              100.0\n",
              "\n",
              "[1000 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c3d53266-cf95-49ad-807d-38a3a75cb263\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Fitness</th>\n",
              "      <th>Máximas Iterações</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.520745</td>\n",
              "      <td>-0.141423</td>\n",
              "      <td>-0.935617</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.048559</td>\n",
              "      <td>-0.548054</td>\n",
              "      <td>-0.935474</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.612844</td>\n",
              "      <td>0.032884</td>\n",
              "      <td>-0.935686</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.511647</td>\n",
              "      <td>0.088807</td>\n",
              "      <td>-0.936217</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.390923</td>\n",
              "      <td>0.790109</td>\n",
              "      <td>-0.930908</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>-0.000005</td>\n",
              "      <td>-0.000989</td>\n",
              "      <td>-0.999965</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>-0.000020</td>\n",
              "      <td>-0.000009</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>0.014401</td>\n",
              "      <td>-0.005306</td>\n",
              "      <td>-0.998797</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>0.000398</td>\n",
              "      <td>-0.000096</td>\n",
              "      <td>-0.999999</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>-0.000008</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3d53266-cf95-49ad-807d-38a3a75cb263')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c3d53266-cf95-49ad-807d-38a3a75cb263 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c3d53266-cf95-49ad-807d-38a3a75cb263');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-13d8ebbb-54e9-4f52-a944-ec66ceca6cb7\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-13d8ebbb-54e9-4f52-a944-ec66ceca6cb7')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-13d8ebbb-54e9-4f52-a944-ec66ceca6cb7 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_1583597d-9ba6-4d0d-93d9-aa31c5838c56\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp2drop_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1583597d-9ba6-4d0d-93d9-aa31c5838c56 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp2drop_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp2drop_df",
              "summary": "{\n  \"name\": \"exp2drop_df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5714731782469449,\n        \"min\": -5.116776247761825,\n        \"max\": 7.352116924845652,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          -0.006932368359671634,\n          -0.00019126328576518387,\n          0.00018212118278401577\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4954097795250908,\n        \"min\": -4.6431373385181605,\n        \"max\": 6.637044535950648,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.0008991221584010586,\n          -0.003097035010379237,\n          0.0023648513234680165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03150851852617473,\n        \"min\": -0.9999999999981937,\n        \"max\": -0.7937279298411761,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          -0.9999703764363869,\n          -0.9999039156549652,\n          -0.999990730423147\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M\\u00e1ximas Itera\\u00e7\\u00f5es\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.73718541934519,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0,\n          60.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "sns.violinplot(data=exp2drop_df, x=\"Máximas Iterações\", y=\"Fitness\", ax=ax,\n",
        "               cut=0, density_norm=\"width\")\n",
        "ax.set_title(\"Experimento 2: Dispersão (Drop-Wave).\")\n",
        "ax.set_ylabel(\"Contagem\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "1b6bea29-c549-4467-a7d0-8af1cfb18514",
        "id": "ooAyFdQc94dE"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exp2drop_filt_df = exp2drop_df.groupby([\"Máximas Iterações\"])[[\"X1\", \"X2\", \"Fitness\"]].agg([\"mean\", \"std\", \"median\"])\n",
        "exp2drop_filt_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "5B1Yn7IkAab5",
        "outputId": "9f2a4450-9f34-43d5-d9f1-45558c7b4c3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         X1                            X2                      \\\n",
              "                       mean       std    median      mean       std    median   \n",
              "Máximas Iterações                                                               \n",
              "10.0               0.120992  1.274929 -0.014541 -0.020596  1.183562  0.034616   \n",
              "20.0               0.108428  1.090616  0.042856 -0.077542  0.803603 -0.010236   \n",
              "30.0              -0.037424  0.324520 -0.019837 -0.037947  0.404239 -0.000424   \n",
              "40.0              -0.026421  0.305209 -0.000006 -0.013904  0.308325 -0.001036   \n",
              "50.0              -0.056842  0.353861 -0.004590  0.020261  0.197202  0.003943   \n",
              "60.0              -0.009497  0.237397 -0.000161  0.021896  0.211051  0.000270   \n",
              "70.0              -0.019173  0.161168 -0.000541  0.004330  0.128988  0.000296   \n",
              "80.0              -0.004619  0.146829 -0.000037 -0.013958  0.172702 -0.000066   \n",
              "90.0               0.001357  0.098117  0.000024  0.009583  0.146109 -0.000080   \n",
              "100.0              0.006822  0.107436 -0.000003 -0.000122  0.110119  0.000005   \n",
              "\n",
              "                    Fitness                      \n",
              "                       mean       std    median  \n",
              "Máximas Iterações                                \n",
              "10.0              -0.927735  0.025744 -0.934853  \n",
              "20.0              -0.948175  0.022229 -0.936216  \n",
              "30.0              -0.956149  0.025153 -0.936244  \n",
              "40.0              -0.971622  0.027398 -0.984085  \n",
              "50.0              -0.977264  0.027199 -0.992940  \n",
              "60.0              -0.981353  0.025759 -0.996090  \n",
              "70.0              -0.992430  0.018288 -0.999730  \n",
              "80.0              -0.988724  0.022994 -0.999904  \n",
              "90.0              -0.994689  0.016333 -0.999985  \n",
              "100.0             -0.994279  0.017661 -0.999999  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-99b57f87-c875-4b97-844f-0c63aea75dfe\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">X1</th>\n",
              "      <th colspan=\"3\" halign=\"left\">X2</th>\n",
              "      <th colspan=\"3\" halign=\"left\">Fitness</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Máximas Iterações</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>0.120992</td>\n",
              "      <td>1.274929</td>\n",
              "      <td>-0.014541</td>\n",
              "      <td>-0.020596</td>\n",
              "      <td>1.183562</td>\n",
              "      <td>0.034616</td>\n",
              "      <td>-0.927735</td>\n",
              "      <td>0.025744</td>\n",
              "      <td>-0.934853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20.0</th>\n",
              "      <td>0.108428</td>\n",
              "      <td>1.090616</td>\n",
              "      <td>0.042856</td>\n",
              "      <td>-0.077542</td>\n",
              "      <td>0.803603</td>\n",
              "      <td>-0.010236</td>\n",
              "      <td>-0.948175</td>\n",
              "      <td>0.022229</td>\n",
              "      <td>-0.936216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30.0</th>\n",
              "      <td>-0.037424</td>\n",
              "      <td>0.324520</td>\n",
              "      <td>-0.019837</td>\n",
              "      <td>-0.037947</td>\n",
              "      <td>0.404239</td>\n",
              "      <td>-0.000424</td>\n",
              "      <td>-0.956149</td>\n",
              "      <td>0.025153</td>\n",
              "      <td>-0.936244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40.0</th>\n",
              "      <td>-0.026421</td>\n",
              "      <td>0.305209</td>\n",
              "      <td>-0.000006</td>\n",
              "      <td>-0.013904</td>\n",
              "      <td>0.308325</td>\n",
              "      <td>-0.001036</td>\n",
              "      <td>-0.971622</td>\n",
              "      <td>0.027398</td>\n",
              "      <td>-0.984085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50.0</th>\n",
              "      <td>-0.056842</td>\n",
              "      <td>0.353861</td>\n",
              "      <td>-0.004590</td>\n",
              "      <td>0.020261</td>\n",
              "      <td>0.197202</td>\n",
              "      <td>0.003943</td>\n",
              "      <td>-0.977264</td>\n",
              "      <td>0.027199</td>\n",
              "      <td>-0.992940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60.0</th>\n",
              "      <td>-0.009497</td>\n",
              "      <td>0.237397</td>\n",
              "      <td>-0.000161</td>\n",
              "      <td>0.021896</td>\n",
              "      <td>0.211051</td>\n",
              "      <td>0.000270</td>\n",
              "      <td>-0.981353</td>\n",
              "      <td>0.025759</td>\n",
              "      <td>-0.996090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70.0</th>\n",
              "      <td>-0.019173</td>\n",
              "      <td>0.161168</td>\n",
              "      <td>-0.000541</td>\n",
              "      <td>0.004330</td>\n",
              "      <td>0.128988</td>\n",
              "      <td>0.000296</td>\n",
              "      <td>-0.992430</td>\n",
              "      <td>0.018288</td>\n",
              "      <td>-0.999730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80.0</th>\n",
              "      <td>-0.004619</td>\n",
              "      <td>0.146829</td>\n",
              "      <td>-0.000037</td>\n",
              "      <td>-0.013958</td>\n",
              "      <td>0.172702</td>\n",
              "      <td>-0.000066</td>\n",
              "      <td>-0.988724</td>\n",
              "      <td>0.022994</td>\n",
              "      <td>-0.999904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90.0</th>\n",
              "      <td>0.001357</td>\n",
              "      <td>0.098117</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.009583</td>\n",
              "      <td>0.146109</td>\n",
              "      <td>-0.000080</td>\n",
              "      <td>-0.994689</td>\n",
              "      <td>0.016333</td>\n",
              "      <td>-0.999985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100.0</th>\n",
              "      <td>0.006822</td>\n",
              "      <td>0.107436</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>-0.000122</td>\n",
              "      <td>0.110119</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>-0.994279</td>\n",
              "      <td>0.017661</td>\n",
              "      <td>-0.999999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99b57f87-c875-4b97-844f-0c63aea75dfe')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-99b57f87-c875-4b97-844f-0c63aea75dfe button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-99b57f87-c875-4b97-844f-0c63aea75dfe');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-c072c30f-8928-46f5-9b8a-ab399c3fe099\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c072c30f-8928-46f5-9b8a-ab399c3fe099')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-c072c30f-8928-46f5-9b8a-ab399c3fe099 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_1e737a89-bbd4-410a-9575-5cff2511cd6f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp2drop_filt_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1e737a89-bbd4-410a-9575-5cff2511cd6f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp2drop_filt_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp2drop_filt_df",
              "summary": "{\n  \"name\": \"exp2drop_filt_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": [\n        \"M\\u00e1ximas Itera\\u00e7\\u00f5es\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.276503540974915,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0,\n          60.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05921113692647057,\n        \"min\": -0.056842292200270386,\n        \"max\": 0.12099188360635765,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.0013567270506318824,\n          0.10842841975460377,\n          -0.009496579483032298\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.41933350569400546,\n        \"min\": 0.09811712835225957,\n        \"max\": 1.274929387075405,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.09811712835225957,\n          1.0906163490561591,\n          0.23739676920599959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016539411938952316,\n        \"min\": -0.01983689218740562,\n        \"max\": 0.042856190699426686,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2.4240392473354873e-05,\n          0.042856190699426686,\n          -0.00016087094461098218\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029998678403534932,\n        \"min\": -0.07754237184209013,\n        \"max\": 0.021896228141892987,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.009583344351393006,\n          -0.07754237184209013,\n          0.021896228141892987\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35348378659925966,\n        \"min\": 0.11011903330071537,\n        \"max\": 1.1835623768340533,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.1461094992733562,\n          0.8036025249726909,\n          0.21105146292723106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011764251493944763,\n        \"min\": -0.010235652187248298,\n        \"max\": 0.03461642812302172,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -7.963229044423442e-05,\n          -0.010235652187248298,\n          0.0002700763679961011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.022574394188225815,\n        \"min\": -0.9946894709858307,\n        \"max\": -0.9277347866846339,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.9946894709858307,\n          -0.9481745264854049,\n          -0.9813529357076775\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00411442237721988,\n        \"min\": 0.016332675471926284,\n        \"max\": 0.02739811512517315,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.016332675471926284,\n          0.022228570075333097,\n          0.025758845303836247\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029548252826065057,\n        \"min\": -0.9999993522021955,\n        \"max\": -0.934852635661917,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.9999850032301356,\n          -0.9362160684905008,\n          -0.9960897000490898\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experimento 2: Excursão do Número de Máximas Iterações (Levi)."
      ],
      "metadata": {
        "id": "hTX8m6CVD89b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dimensions of the problem\n",
        "exp2levi_data = []\n",
        "INIT = 100\n",
        "dim = 2\n",
        "objective = levi\n",
        "for max_iter in range(10, 101, 10):\n",
        "  for i in range(INIT):\n",
        "    print(f\"\\rMax.iter. {max_iter}, Inicialização {i}...\", end='')\n",
        "    solution, fitness, _ = pso(objective, dim=dim, max_iter=max_iter) #Mantivemos o w=0.5\n",
        "    solution = np.append(solution, fitness)\n",
        "    solution = np.append(solution, max_iter)\n",
        "    exp2levi_data.append(solution)\n",
        "exp2levi_data = np.array(exp2levi_data)\n",
        "exp2levi_df = pd.DataFrame(exp2levi_data, columns=['X1', 'X2', 'Fitness', 'Máximas Iterações'])\n",
        "exp2levi_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "7032e79f-70da-4e27-a0eb-fd4c561d26d2",
        "id": "BU-5x6jaD89c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max.iter. 100, Inicialização 99..."
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           X1        X2       Fitness  Máximas Iterações\n",
              "0    1.103400  1.141834  2.814194e-02               10.0\n",
              "1    0.894469  0.833839  1.422487e-02               10.0\n",
              "2    1.132614  0.082344  1.965535e-01               10.0\n",
              "3    1.011673  1.148391  4.547104e-02               10.0\n",
              "4    0.987971  0.939510  1.714703e-02               10.0\n",
              "..        ...       ...           ...                ...\n",
              "995  1.000000  1.000000  1.777646e-14              100.0\n",
              "996  1.000000  1.000000  8.697575e-21              100.0\n",
              "997  1.000000  1.000000  3.033878e-19              100.0\n",
              "998  1.000000  1.000000  2.074015e-17              100.0\n",
              "999  1.000000  1.000000  2.919816e-15              100.0\n",
              "\n",
              "[1000 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-de91b1b5-e051-4cb3-936e-d9b23d8c55ff\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Fitness</th>\n",
              "      <th>Máximas Iterações</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.103400</td>\n",
              "      <td>1.141834</td>\n",
              "      <td>2.814194e-02</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.894469</td>\n",
              "      <td>0.833839</td>\n",
              "      <td>1.422487e-02</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.132614</td>\n",
              "      <td>0.082344</td>\n",
              "      <td>1.965535e-01</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.011673</td>\n",
              "      <td>1.148391</td>\n",
              "      <td>4.547104e-02</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.987971</td>\n",
              "      <td>0.939510</td>\n",
              "      <td>1.714703e-02</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.777646e-14</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.697575e-21</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.033878e-19</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.074015e-17</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.919816e-15</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de91b1b5-e051-4cb3-936e-d9b23d8c55ff')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-de91b1b5-e051-4cb3-936e-d9b23d8c55ff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-de91b1b5-e051-4cb3-936e-d9b23d8c55ff');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-fb26ae20-eba3-4e22-921f-4beab2d79607\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb26ae20-eba3-4e22-921f-4beab2d79607')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-fb26ae20-eba3-4e22-921f-4beab2d79607 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_bcd3011b-7642-4323-9fdb-5a309f9c708e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp2levi_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bcd3011b-7642-4323-9fdb-5a309f9c708e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp2levi_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp2levi_df",
              "summary": "{\n  \"name\": \"exp2levi_df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12417898244026652,\n        \"min\": -1.3319548609818794,\n        \"max\": 1.7504156017307548,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.9999938669259983,\n          0.9999998783721202,\n          0.9999999979513132\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11184176221334705,\n        \"min\": -0.0914194202269819,\n        \"max\": 2.4738621787715385,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          1.0000993411219352,\n          0.9999995346926454,\n          1.000000027783405\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026026974451773637,\n        \"min\": 9.73491074529323e-23,\n        \"max\": 0.2729307538772539,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          2.387650796210182e-10,\n          1.8172294622126005e-13,\n          1.1489297248091023e-15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M\\u00e1ximas Itera\\u00e7\\u00f5es\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.73718541934519,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0,\n          60.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "sns.violinplot(data=exp2levi_df, x=\"Máximas Iterações\", y=\"Fitness\", ax=ax,\n",
        "               cut=0, density_norm=\"width\")\n",
        "ax.set_title(\"Experimento 2: Dispersão (Levi).\")\n",
        "ax.set_ylabel(\"Contagem\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "b0ddd644-2ff9-4197-873f-d0630106e11e",
        "id": "aAnvINLCD89c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exp2levi_filt_df = exp2levi_df.groupby([\"Máximas Iterações\"])[[\"X1\", \"X2\", \"Fitness\"]].agg([\"mean\", \"std\", \"median\"])\n",
        "exp2levi_filt_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "outputId": "af1623dd-4637-4be3-c999-6120d94fb9f8",
        "id": "CHMPCq6uD89d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         X1                                X2            \\\n",
              "                       mean           std    median      mean       std   \n",
              "Máximas Iterações                                                         \n",
              "10.0               0.964960  3.811296e-01  0.999190  1.034628  0.344422   \n",
              "20.0               1.009544  9.457584e-02  0.999849  0.995125  0.078532   \n",
              "30.0               1.000095  1.155466e-02  1.000028  0.996826  0.015599   \n",
              "40.0               0.999884  1.254601e-03  0.999996  1.000513  0.003560   \n",
              "50.0               1.000015  1.288703e-04  0.999999  0.999944  0.001220   \n",
              "60.0               1.000010  5.097211e-05  1.000000  1.000018  0.000151   \n",
              "70.0               1.000001  6.760932e-06  1.000000  0.999997  0.000041   \n",
              "80.0               1.000000  9.471034e-07  1.000000  0.999999  0.000007   \n",
              "90.0               1.000000  3.557444e-08  1.000000  1.000000  0.000001   \n",
              "100.0              1.000000  2.824581e-08  1.000000  0.999999  0.000009   \n",
              "\n",
              "                                  Fitness                              \n",
              "                     median          mean           std        median  \n",
              "Máximas Iterações                                                      \n",
              "10.0               1.004286  5.374493e-02  6.377866e-02  2.698072e-02  \n",
              "20.0               0.995657  3.597846e-03  1.303222e-02  6.400147e-04  \n",
              "30.0               0.999628  4.817008e-05  1.374025e-04  5.246315e-06  \n",
              "40.0               0.999995  2.585908e-06  7.462799e-06  2.287790e-07  \n",
              "50.0               0.999997  9.258038e-08  3.425443e-07  3.078610e-09  \n",
              "60.0               1.000002  1.193234e-08  7.370576e-08  1.617110e-10  \n",
              "70.0               1.000000  7.591340e-11  2.953579e-10  1.433843e-12  \n",
              "80.0               1.000000  2.080964e-11  1.933567e-10  4.019862e-14  \n",
              "90.0               1.000000  1.311748e-12  1.135279e-11  1.582330e-15  \n",
              "100.0              1.000000  9.188669e-15  5.698062e-14  1.083657e-17  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ba40156b-f6dc-4f01-b5a0-8b1504fa5d82\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">X1</th>\n",
              "      <th colspan=\"3\" halign=\"left\">X2</th>\n",
              "      <th colspan=\"3\" halign=\"left\">Fitness</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Máximas Iterações</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>0.964960</td>\n",
              "      <td>3.811296e-01</td>\n",
              "      <td>0.999190</td>\n",
              "      <td>1.034628</td>\n",
              "      <td>0.344422</td>\n",
              "      <td>1.004286</td>\n",
              "      <td>5.374493e-02</td>\n",
              "      <td>6.377866e-02</td>\n",
              "      <td>2.698072e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20.0</th>\n",
              "      <td>1.009544</td>\n",
              "      <td>9.457584e-02</td>\n",
              "      <td>0.999849</td>\n",
              "      <td>0.995125</td>\n",
              "      <td>0.078532</td>\n",
              "      <td>0.995657</td>\n",
              "      <td>3.597846e-03</td>\n",
              "      <td>1.303222e-02</td>\n",
              "      <td>6.400147e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30.0</th>\n",
              "      <td>1.000095</td>\n",
              "      <td>1.155466e-02</td>\n",
              "      <td>1.000028</td>\n",
              "      <td>0.996826</td>\n",
              "      <td>0.015599</td>\n",
              "      <td>0.999628</td>\n",
              "      <td>4.817008e-05</td>\n",
              "      <td>1.374025e-04</td>\n",
              "      <td>5.246315e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40.0</th>\n",
              "      <td>0.999884</td>\n",
              "      <td>1.254601e-03</td>\n",
              "      <td>0.999996</td>\n",
              "      <td>1.000513</td>\n",
              "      <td>0.003560</td>\n",
              "      <td>0.999995</td>\n",
              "      <td>2.585908e-06</td>\n",
              "      <td>7.462799e-06</td>\n",
              "      <td>2.287790e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50.0</th>\n",
              "      <td>1.000015</td>\n",
              "      <td>1.288703e-04</td>\n",
              "      <td>0.999999</td>\n",
              "      <td>0.999944</td>\n",
              "      <td>0.001220</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>9.258038e-08</td>\n",
              "      <td>3.425443e-07</td>\n",
              "      <td>3.078610e-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60.0</th>\n",
              "      <td>1.000010</td>\n",
              "      <td>5.097211e-05</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000018</td>\n",
              "      <td>0.000151</td>\n",
              "      <td>1.000002</td>\n",
              "      <td>1.193234e-08</td>\n",
              "      <td>7.370576e-08</td>\n",
              "      <td>1.617110e-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70.0</th>\n",
              "      <td>1.000001</td>\n",
              "      <td>6.760932e-06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>0.000041</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.591340e-11</td>\n",
              "      <td>2.953579e-10</td>\n",
              "      <td>1.433843e-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80.0</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.471034e-07</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999999</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.080964e-11</td>\n",
              "      <td>1.933567e-10</td>\n",
              "      <td>4.019862e-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90.0</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.557444e-08</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.311748e-12</td>\n",
              "      <td>1.135279e-11</td>\n",
              "      <td>1.582330e-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100.0</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.824581e-08</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999999</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.188669e-15</td>\n",
              "      <td>5.698062e-14</td>\n",
              "      <td>1.083657e-17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba40156b-f6dc-4f01-b5a0-8b1504fa5d82')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ba40156b-f6dc-4f01-b5a0-8b1504fa5d82 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ba40156b-f6dc-4f01-b5a0-8b1504fa5d82');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-a6988051-21d0-4d0b-9a6e-7a6f99721a05\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a6988051-21d0-4d0b-9a6e-7a6f99721a05')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-a6988051-21d0-4d0b-9a6e-7a6f99721a05 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_6e0b2075-1d26-4f50-a246-6ec142310747\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp2levi_filt_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6e0b2075-1d26-4f50-a246-6ec142310747 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp2levi_filt_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp2levi_filt_df",
              "summary": "{\n  \"name\": \"exp2levi_filt_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": [\n        \"M\\u00e1ximas Itera\\u00e7\\u00f5es\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.276503540974915,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0,\n          60.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011803599329274321,\n        \"min\": 0.9649602130176672,\n        \"max\": 1.0095442204330227,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.9999999992405448,\n          1.0095442204330227,\n          1.000009730689723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.120395859857103,\n        \"min\": 2.824581014834718e-08,\n        \"max\": 0.3811295961949796,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3.5574440529723095e-08,\n          0.09457584330460976,\n          5.097211312834053e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0002563462604670373,\n        \"min\": 0.9991902206503445,\n        \"max\": 1.0000278216690373,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.0000000001311236,\n          0.9998492004030726,\n          1.0000001484846233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011353232369679804,\n        \"min\": 0.9951254302205599,\n        \"max\": 1.0346284739553253,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.9999999264403736,\n          0.9951254302205599,\n          1.0000184515940216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10820668708992326,\n        \"min\": 1.1428377636333553e-06,\n        \"max\": 0.34442184463393705,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.1428377636333553e-06,\n          0.07853175962270594,\n          0.00015132454920928636\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0020371879088569183,\n        \"min\": 0.9956571788707196,\n        \"max\": 1.004286186166402,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.0000000001470717,\n          0.9956571788707196,\n          1.0000018516336748\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016905163405671348,\n        \"min\": 9.188669453723705e-15,\n        \"max\": 0.05374492558179176,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.3117476140180719e-12,\n          0.0035978458237626828,\n          1.1932341399543769e-08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020125577238678666,\n        \"min\": 5.698062027102353e-14,\n        \"max\": 0.06377865703774882,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.1352794076203662e-11,\n          0.013032218842557357,\n          7.370575977353902e-08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008511744545188741,\n        \"min\": 1.0836574597162703e-17,\n        \"max\": 0.026980720481249804,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.5823298978735969e-15,\n          0.0006400146832886064,\n          1.617110432557552e-10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experimento 3 (Iterações): Excursão do Número de Inicializações (Drop-Wave)."
      ],
      "metadata": {
        "id": "2vSehtA-F0GB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dimensions of the problem\n",
        "exp3drop_data = []\n",
        "dim = 2\n",
        "objective = drop_wave\n",
        "for num_init in range(10, 101, 10):\n",
        "  for i in range(num_init):\n",
        "    print(f\"\\rN de Inicializações. {num_init}, Inicialização {i}...\", end='')\n",
        "    solution, fitness, _ = pso(objective, dim=dim, w=0.7, max_iter=150) #Mudamos o W\n",
        "    solution = np.append(solution, fitness)\n",
        "    solution = np.append(solution, num_init)\n",
        "    exp3drop_data.append(solution)\n",
        "exp3drop_data = np.array(exp3drop_data)\n",
        "exp3drop_df = pd.DataFrame(exp3drop_data, columns=['X1', 'X2', 'Fitness', 'Inicializações'])\n",
        "exp3drop_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "78e03514-b752-4a7f-ea4a-5ad9084c638c",
        "id": "1vq_XUMDF0GC"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "N de Inicializações. 100, Inicialização 99..."
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               X1            X2   Fitness  Inicializações\n",
              "0    4.890225e-07  1.334543e-05 -1.000000            10.0\n",
              "1    4.082937e-05  1.968675e-05 -1.000000            10.0\n",
              "2   -3.388822e-04  5.493090e-06 -1.000000            10.0\n",
              "3    1.212983e-05 -1.968982e-06 -1.000000            10.0\n",
              "4   -4.698506e-01  2.233582e-01 -0.936245            10.0\n",
              "..            ...           ...       ...             ...\n",
              "545 -1.165730e-04  2.592163e-03 -1.000000           100.0\n",
              "546  2.491977e-07 -3.438022e-07 -1.000000           100.0\n",
              "547 -6.405175e-05  1.153331e-05 -1.000000           100.0\n",
              "548  8.967742e-07 -3.084334e-07 -1.000000           100.0\n",
              "549  1.210058e-02  2.203220e-03 -0.999509           100.0\n",
              "\n",
              "[550 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0731f8c-8c23-430b-8676-8da7759299e4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Fitness</th>\n",
              "      <th>Inicializações</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.890225e-07</td>\n",
              "      <td>1.334543e-05</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.082937e-05</td>\n",
              "      <td>1.968675e-05</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-3.388822e-04</td>\n",
              "      <td>5.493090e-06</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.212983e-05</td>\n",
              "      <td>-1.968982e-06</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-4.698506e-01</td>\n",
              "      <td>2.233582e-01</td>\n",
              "      <td>-0.936245</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>545</th>\n",
              "      <td>-1.165730e-04</td>\n",
              "      <td>2.592163e-03</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>2.491977e-07</td>\n",
              "      <td>-3.438022e-07</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>547</th>\n",
              "      <td>-6.405175e-05</td>\n",
              "      <td>1.153331e-05</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>8.967742e-07</td>\n",
              "      <td>-3.084334e-07</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>1.210058e-02</td>\n",
              "      <td>2.203220e-03</td>\n",
              "      <td>-0.999509</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>550 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0731f8c-8c23-430b-8676-8da7759299e4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c0731f8c-8c23-430b-8676-8da7759299e4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c0731f8c-8c23-430b-8676-8da7759299e4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-372414e7-ee98-4121-a1ab-b589627c1c15\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-372414e7-ee98-4121-a1ab-b589627c1c15')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-372414e7-ee98-4121-a1ab-b589627c1c15 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_6a65e9a8-ed98-480e-b238-013b9545865c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp3drop_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6a65e9a8-ed98-480e-b238-013b9545865c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp3drop_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp3drop_df",
              "summary": "{\n  \"name\": \"exp3drop_df\",\n  \"rows\": 550,\n  \"fields\": [\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12360580071355264,\n        \"min\": -0.5114888677574245,\n        \"max\": 0.5194464828902532,\n        \"num_unique_values\": 550,\n        \"samples\": [\n          1.9443493775410957e-06,\n          3.864696038348085e-06,\n          9.084453580395864e-07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11937465418687353,\n        \"min\": -0.5097904303036949,\n        \"max\": 0.5181914383773899,\n        \"num_unique_values\": 550,\n        \"samples\": [\n          -4.422694068080772e-06,\n          8.200187837070121e-07,\n          -6.958115193330247e-08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01976090886361125,\n        \"min\": -1.0,\n        \"max\": -0.9362444521930903,\n        \"num_unique_values\": 538,\n        \"samples\": [\n          -0.9999999999838451,\n          -0.999999999999057,\n          -0.9999999952926337\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inicializa\\u00e7\\u00f5es\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.517195928059625,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0,\n          60.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "sns.violinplot(data=exp3drop_df, x=\"Inicializações\", y=\"Fitness\", ax=ax,\n",
        "               cut=0, density_norm=\"width\")\n",
        "ax.set_title(\"Experimento 3: Dispersão (Drop-Wave).\")\n",
        "ax.set_ylabel(\"Contagem\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "45b9396c-3742-4ba6-d64c-796ed6c8ed41",
        "id": "wnF2Lk-8F0GD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exp3drop_filt_df = exp3drop_df.groupby([\"Inicializações\"])[[\"X1\", \"X2\", \"Fitness\"]].agg([\"mean\", \"std\", \"median\"])\n",
        "exp3drop_filt_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "420ca220-a412-42fd-9170-009567ec90c6",
        "id": "v0Pt9v7uF0GD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      X1                            X2                      \\\n",
              "                    mean       std    median      mean       std    median   \n",
              "Inicializações                                                               \n",
              "10.0            0.034978  0.101740  0.000664  0.039058  0.119950  0.000240   \n",
              "20.0            0.016107  0.125150  0.000010 -0.021559  0.109760  0.000011   \n",
              "30.0            0.036631  0.111510  0.000037 -0.010466  0.118861 -0.000004   \n",
              "40.0            0.040267  0.144877  0.000012  0.010437  0.144024  0.000007   \n",
              "50.0            0.006583  0.126263 -0.000001 -0.017580  0.147177  0.000011   \n",
              "60.0           -0.009946  0.108925 -0.000013 -0.020816  0.112943 -0.000056   \n",
              "70.0           -0.002374  0.098573  0.000011  0.000417  0.088288 -0.000040   \n",
              "80.0            0.015138  0.118130  0.000025 -0.006671  0.099219 -0.000008   \n",
              "90.0            0.015591  0.110135 -0.000024  0.002478  0.057061 -0.000010   \n",
              "100.0          -0.000334  0.119723  0.000002  0.009782  0.149323 -0.000002   \n",
              "\n",
              "                 Fitness                      \n",
              "                    mean       std    median  \n",
              "Inicializações                                \n",
              "10.0           -0.993414  0.020099 -0.999990  \n",
              "20.0           -0.993617  0.019621 -0.999999  \n",
              "30.0           -0.993494  0.019414 -0.999997  \n",
              "40.0           -0.991896  0.021309 -0.999993  \n",
              "50.0           -0.990958  0.022304 -0.999995  \n",
              "60.0           -0.993508  0.018135 -0.999995  \n",
              "70.0           -0.995368  0.015201 -0.999998  \n",
              "80.0           -0.994299  0.018096 -0.999999  \n",
              "90.0           -0.996135  0.014720 -0.999999  \n",
              "100.0          -0.991587  0.021207 -0.999997  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70f90bc6-08c0-4acc-8424-d8d3413239d1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">X1</th>\n",
              "      <th colspan=\"3\" halign=\"left\">X2</th>\n",
              "      <th colspan=\"3\" halign=\"left\">Fitness</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Inicializações</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>0.034978</td>\n",
              "      <td>0.101740</td>\n",
              "      <td>0.000664</td>\n",
              "      <td>0.039058</td>\n",
              "      <td>0.119950</td>\n",
              "      <td>0.000240</td>\n",
              "      <td>-0.993414</td>\n",
              "      <td>0.020099</td>\n",
              "      <td>-0.999990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20.0</th>\n",
              "      <td>0.016107</td>\n",
              "      <td>0.125150</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>-0.021559</td>\n",
              "      <td>0.109760</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>-0.993617</td>\n",
              "      <td>0.019621</td>\n",
              "      <td>-0.999999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30.0</th>\n",
              "      <td>0.036631</td>\n",
              "      <td>0.111510</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>-0.010466</td>\n",
              "      <td>0.118861</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-0.993494</td>\n",
              "      <td>0.019414</td>\n",
              "      <td>-0.999997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40.0</th>\n",
              "      <td>0.040267</td>\n",
              "      <td>0.144877</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.010437</td>\n",
              "      <td>0.144024</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>-0.991896</td>\n",
              "      <td>0.021309</td>\n",
              "      <td>-0.999993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50.0</th>\n",
              "      <td>0.006583</td>\n",
              "      <td>0.126263</td>\n",
              "      <td>-0.000001</td>\n",
              "      <td>-0.017580</td>\n",
              "      <td>0.147177</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>-0.990958</td>\n",
              "      <td>0.022304</td>\n",
              "      <td>-0.999995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60.0</th>\n",
              "      <td>-0.009946</td>\n",
              "      <td>0.108925</td>\n",
              "      <td>-0.000013</td>\n",
              "      <td>-0.020816</td>\n",
              "      <td>0.112943</td>\n",
              "      <td>-0.000056</td>\n",
              "      <td>-0.993508</td>\n",
              "      <td>0.018135</td>\n",
              "      <td>-0.999995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70.0</th>\n",
              "      <td>-0.002374</td>\n",
              "      <td>0.098573</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>0.000417</td>\n",
              "      <td>0.088288</td>\n",
              "      <td>-0.000040</td>\n",
              "      <td>-0.995368</td>\n",
              "      <td>0.015201</td>\n",
              "      <td>-0.999998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80.0</th>\n",
              "      <td>0.015138</td>\n",
              "      <td>0.118130</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>-0.006671</td>\n",
              "      <td>0.099219</td>\n",
              "      <td>-0.000008</td>\n",
              "      <td>-0.994299</td>\n",
              "      <td>0.018096</td>\n",
              "      <td>-0.999999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90.0</th>\n",
              "      <td>0.015591</td>\n",
              "      <td>0.110135</td>\n",
              "      <td>-0.000024</td>\n",
              "      <td>0.002478</td>\n",
              "      <td>0.057061</td>\n",
              "      <td>-0.000010</td>\n",
              "      <td>-0.996135</td>\n",
              "      <td>0.014720</td>\n",
              "      <td>-0.999999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100.0</th>\n",
              "      <td>-0.000334</td>\n",
              "      <td>0.119723</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.009782</td>\n",
              "      <td>0.149323</td>\n",
              "      <td>-0.000002</td>\n",
              "      <td>-0.991587</td>\n",
              "      <td>0.021207</td>\n",
              "      <td>-0.999997</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70f90bc6-08c0-4acc-8424-d8d3413239d1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-70f90bc6-08c0-4acc-8424-d8d3413239d1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-70f90bc6-08c0-4acc-8424-d8d3413239d1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-930804bd-ba61-4f9e-a0cc-f5e6cd90ce55\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-930804bd-ba61-4f9e-a0cc-f5e6cd90ce55')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-930804bd-ba61-4f9e-a0cc-f5e6cd90ce55 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_b1b3f0ef-3186-48c8-95fa-88e4362f8226\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp3drop_filt_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b1b3f0ef-3186-48c8-95fa-88e4362f8226 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp3drop_filt_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp3drop_filt_df",
              "summary": "{\n  \"name\": \"exp3drop_filt_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": [\n        \"Inicializa\\u00e7\\u00f5es\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.276503540974915,\n        \"min\": 10.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0,\n          60.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.017437242778995207,\n        \"min\": -0.009946356458155252,\n        \"max\": 0.040266834217330585,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.015591272272833342,\n          0.016107204050422236,\n          -0.009946356458155252\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013541939200765355,\n        \"min\": 0.0985727121948535,\n        \"max\": 0.14487656173220084,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.1101353058525792,\n          0.12514989189445744,\n          0.10892517238342014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00020864758361900182,\n        \"min\": -2.4478577232022867e-05,\n        \"max\": 0.0006637507514705258,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -2.4478577232022867e-05,\n          9.77456347309512e-06,\n          -1.332350397027646e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018485498690873677,\n        \"min\": -0.021559478970054398,\n        \"max\": 0.039058001376276005,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.0024781381342082673,\n          -0.021559478970054398,\n          -0.020816169794250928\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.028762078032185147,\n        \"min\": 0.05706079140395269,\n        \"max\": 0.14932278133904867,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.05706079140395269,\n          0.10975994677881123,\n          0.1129433239391549\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.210552953441026e-05,\n        \"min\": -5.625804067449394e-05,\n        \"max\": 0.0002400760207021971,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -9.902110984926735e-06,\n          1.0599161433103208e-05,\n          -5.625804067449394e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0016241749642976065,\n        \"min\": -0.9961354784209233,\n        \"max\": -0.9909575330150493,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.9961354784209233,\n          -0.9936170806513587,\n          -0.99350818320366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002520868637827866,\n        \"min\": 0.014719650950120428,\n        \"max\": 0.0223036125473796,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.014719650950120428,\n          0.01962073965448111,\n          0.018134982605832503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9681178294085746e-06,\n        \"min\": -0.9999993305354293,\n        \"max\": -0.9999902988594875,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.9999985907196085,\n          -0.9999993305354293,\n          -0.9999950029798257\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experimento 3A (Indivíduos): Excursão do Número de Inicializações (Drop-Wave)."
      ],
      "metadata": {
        "id": "2NRaktu0Jj6i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dimensions of the problem\n",
        "exp3dropA_data = []\n",
        "dim = 2\n",
        "objective = drop_wave\n",
        "for num_ind in range(10, 100, 10):\n",
        "  for i in range(INIT):\n",
        "    print(f\"\\rN de Indivíduos. {num_ind}, Inicialização {i}...\", end='')\n",
        "    solution, fitness, _ = pso(objective, dim=dim, w=0.7, max_iter=50, num_particles=num_ind) #Mudamos o W\n",
        "    solution = np.append(solution, fitness)\n",
        "    solution = np.append(solution, num_ind)\n",
        "    exp3dropA_data.append(solution)\n",
        "exp3dropA_data = np.array(exp3dropA_data)\n",
        "exp3dropA_df = pd.DataFrame(exp3dropA_data, columns=['X1', 'X2', 'Fitness', 'Indivíduos'])\n",
        "exp3dropA_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "f0a43faa-1894-4417-a99a-f8da4331d98d",
        "id": "n-B3XPJnJj6j"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "N de Indivíduos. 90, Inicialização 99..."
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           X1        X2   Fitness  Indivíduos\n",
              "0   -0.115608  0.485514 -0.936015        10.0\n",
              "1   -0.216050  0.094797 -0.935117        10.0\n",
              "2    0.148090  0.319441 -0.935903        10.0\n",
              "3    0.101168  0.238505 -0.932768        10.0\n",
              "4   -0.383066  0.388000 -0.936245        10.0\n",
              "..        ...       ...       ...         ...\n",
              "895 -0.370792 -0.127037 -0.987818        90.0\n",
              "896 -0.033007  0.061755 -0.997223        90.0\n",
              "897  0.062565 -0.031398 -0.997215        90.0\n",
              "898  0.007578  0.070618 -0.978589        90.0\n",
              "899  0.068360  0.022969 -0.997676        90.0\n",
              "\n",
              "[900 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5379396f-fe0c-4518-9bc8-0869fd1c82f5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>Fitness</th>\n",
              "      <th>Indivíduos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.115608</td>\n",
              "      <td>0.485514</td>\n",
              "      <td>-0.936015</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.216050</td>\n",
              "      <td>0.094797</td>\n",
              "      <td>-0.935117</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.148090</td>\n",
              "      <td>0.319441</td>\n",
              "      <td>-0.935903</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.101168</td>\n",
              "      <td>0.238505</td>\n",
              "      <td>-0.932768</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.383066</td>\n",
              "      <td>0.388000</td>\n",
              "      <td>-0.936245</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>-0.370792</td>\n",
              "      <td>-0.127037</td>\n",
              "      <td>-0.987818</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896</th>\n",
              "      <td>-0.033007</td>\n",
              "      <td>0.061755</td>\n",
              "      <td>-0.997223</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>897</th>\n",
              "      <td>0.062565</td>\n",
              "      <td>-0.031398</td>\n",
              "      <td>-0.997215</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>898</th>\n",
              "      <td>0.007578</td>\n",
              "      <td>0.070618</td>\n",
              "      <td>-0.978589</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>899</th>\n",
              "      <td>0.068360</td>\n",
              "      <td>0.022969</td>\n",
              "      <td>-0.997676</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>900 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5379396f-fe0c-4518-9bc8-0869fd1c82f5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5379396f-fe0c-4518-9bc8-0869fd1c82f5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5379396f-fe0c-4518-9bc8-0869fd1c82f5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-b5bb3f80-19b0-471b-9fb5-398b91243f2b\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b5bb3f80-19b0-471b-9fb5-398b91243f2b')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-b5bb3f80-19b0-471b-9fb5-398b91243f2b button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_dfe72977-d76a-4039-add1-1034d270b08d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp3dropA_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_dfe72977-d76a-4039-add1-1034d270b08d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp3dropA_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp3dropA_df",
              "summary": "{\n  \"name\": \"exp3dropA_df\",\n  \"rows\": 900,\n  \"fields\": [\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22590996795348744,\n        \"min\": -1.0447596072197278,\n        \"max\": 1.8816824601215913,\n        \"num_unique_values\": 900,\n        \"samples\": [\n          -0.49713698665583933,\n          -0.01067283258746715,\n          0.06933025181464932\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2131000562705515,\n        \"min\": -1.4685296154831637,\n        \"max\": 1.9662150968102,\n        \"num_unique_values\": 900,\n        \"samples\": [\n          0.02251946586305123,\n          0.0006695233309035331,\n          -0.5167086325289555\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025021843643478543,\n        \"min\": -0.9999999825594068,\n        \"max\": -0.9327678101883707,\n        \"num_unique_values\": 900,\n        \"samples\": [\n          -0.9361241211484065,\n          -0.9999764439068856,\n          -0.9362452851980007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Indiv\\u00edduos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.834245322211114,\n        \"min\": 10.0,\n        \"max\": 90.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          80.0,\n          20.0,\n          60.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "sns.violinplot(data=exp3dropA_df, x=\"Indivíduos\", y=\"Fitness\", ax=ax,\n",
        "               cut=0, density_norm=\"width\")\n",
        "ax.set_title(\"Experimento 3A: Dispersão (Drop-Wave).\")\n",
        "ax.set_ylabel(\"Contagem\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "e262e997-3834-4c9c-d4cd-e52787882602",
        "id": "YBMl03pMJj6j"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAHHCAYAAACvJxw8AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAy6xJREFUeJzsnXmYFNW5/79Vvc707AvMsA64sbiDokjUCCKaaDQao5IYvYpGgxoiUbkmv8QsinFJIupVs3BJxA2XKHpFwRFlR2VREBCGfRaGoWftmenuqjq/P6pP9XRP713dfarmfJ5nHpjuqupTU9VV3/q+73lfgRBCwOFwOBwOh8OJiJjrAXA4HA6Hw+GwDBdLHA6Hw+FwODHgYonD4XA4HA4nBlwscTgcDofD4cSAiyUOh8PhcDicGHCxxOFwOBwOhxMDLpY4HA6Hw+FwYsDFEofD4XA4HE4MuFjicDgcDofDiQEXSxxOGvz2t7+FIAi5HgYnCf73f/8XgiBg//79uR5K2sydOxeFhYX4yU9+ArfbjXHjxmHLli26f87GjRtht9tx4MAB3bfNic8555yD++67L9fDGNBwscTJKfTGFe1n/fr1uR6iKXj44Yfxn//8R/ftzpkzB2eeeSbKysqQn5+PsWPH4re//S26urqirvPss89CEARMmjQp7c9fuXJlyPnicDgwePBgXHjhhXj44Ydx9OjRtD+DVbq6uvA///M/+N3vfoft27ejoqICBQUFOPXUU3X/rAcffBDXX389Ro4cqb124YUXan93URRRVFSEk046CT/+8Y+xfPly3ceQLn/6058gCAI2b94c8johBKWlpRAEAfv27Qt5r7e3Fw6HAzfccEM2h9qP+++/H8888wyamppyOo6BjMB7w3Fyyf/+7//i5ptvxu9+9zuMGjWq3/szZsxARUVFDkaWGJIkQZIkOJ3OXA8lJgUFBbjmmmvwv//7v7pud8qUKZgwYQKOP/54OJ1ObN68Gf/85z8xceJEfPrppxDF/s9j5513HhoaGrB//37s3r0bxx9/fMqfv3LlSnz729/G3XffjbPOOguyLOPo0aNYu3Ytli5diuLiYrz22mu46KKLtHVkWYbf74fD4TC0KyhJEurr6zUB09DQgKqqqoh/83TYsmULzjjjDKxduxbnnnuu9vqFF16Iuro6PPLIIwAAj8eDPXv24M0338TevXtx7bXX4sUXX4TNZtN1PKmydu1anHfeeXjqqadw1113aa9v27YNp5xyCqxWKxYuXIgf/ehH2nurVq3C+eefj2eeeQZ33nlnLoYNAFAUBUOHDsWsWbPwu9/9LmfjGNAQDieHLFy4kAAgn332Wa6HkhRdXV25HkJSuFwu8pOf/CQrn/X4448TAGTdunX93tu7dy8BQN58801SWVlJfvvb36b1WR9//DEBQJYsWdLvvS1btpBBgwaRkpIS0tDQkNbnZAsWz6u7776bjBgxgiiKEvL6BRdcQMaPH99veUmSyJ133kkAkPvuuy/mtmVZJj09PbqONxper5c4nU5y7bXXhrz+3HPPkfLycnLJJZeQ22+/PeS9hx9+mAAgW7duzcoYYzF79mwycuTIfseBkx14GI5jCH7zm99AFEV89NFHIa/fdtttsNvt2Lp1K4BgWObVV1/Ff//3f6OqqgoulwtXXHEFDh061G+7GzZswIwZM1BcXIz8/HxccMEFWLNmTcgyNC/p66+/xg033IDS0lJMmTIl5L2+CIKA2bNnY8mSJRg3bhzy8vJw7rnn4quvvgIAPP/885oTc+GFF0bMnUlmXHv27MFNN92EkpISFBcX4+abb0Z3d3fIeDweDxYtWqSFTW666Sbt/c2bN+PSSy9FUVERCgoKMHXq1LTCnzU1NQCAtra2fu8tXrwYpaWl+M53voNrrrkGixcvjriNuro61NXVpTwGADjttNPwl7/8BW1tbXj66ae11yPlLH3++ee45JJLUFFRgby8PIwaNQr/9V//pb2/f/9+CIKAxx9/HH/+858xcuRI5OXl4YILLsC2bdv6ffbOnTtxzTXXoKysDE6nExMnTsQ777wTsgwdxyeffII777wTgwYNwrBhwwAAnZ2d+PnPf46amho4HA4MGjQIF198MTZt2qStv3LlSlxzzTUYMWIEHA4Hhg8fjjlz5qCnp6ffeGpra/Gtb30LLpcLJSUl+N73vocdO3Yk9Hf8z3/+g4suuihhF85iseCpp57CuHHj8PTTT6O9vV17j343Fi9ejPHjx8PhcGDZsmUAEjsP6d/s008/xe23347y8nIUFRXhxhtvRGtra8xx2e12nHXWWf2+R2vWrMG5556L8847L+J7JSUlOPnkkwEAjz/+OCZPnozy8nLk5eVhwoQJeP3110PWOfnkk/Htb3+73+dTd+iaa64Jee0vf/kLxo8fD6fTicGDB+P222+PuC8XX3wxDhw4kJGcNE4C5FqtcQY21FlasWIFOXr0aMhPS0uLtpzP5yNnnHEGGTlyJOno6CCEELJs2TICgPz+97/XlqNOwymnnEJOPfVU8uSTT5IHHniAOJ1OcuKJJ5Lu7m5t2Y8++ojY7XZy7rnnkieeeIL8+c9/Jqeeeiqx2+1kw4YN2nK/+c1vCAAybtw48r3vfY88++yz5Jlnngl5ry8AyKmnnkqGDx9O5s+fT+bPn0+Ki4vJiBEjyNNPP03GjRtHnnjiCfKrX/2K2O128u1vfztk/WTHdcYZZ5Dvf//75NlnnyW33nprvyf6f//738ThcJBvfetb5N///jf597//TdauXUsIIWTbtm3E5XKR6upq8vvf/57Mnz+fjBo1ijgcDrJ+/fqEjqHf7ydHjx4l9fX15IMPPiBjxowhhYWF5NixY/2WHTNmDLnlllsIIYR8+umnBADZuHFjv+VGjhxJRo4cGfezYzlLhKjnTV5eHpk4caL2Gj3n9u3bRwgh5MiRI6S0tJSceOKJ5LHHHiN/+9vfyIMPPkjGjh2rrbNv3z7tvKqpqSGPPvooeeihh0hZWRmprKwkTU1N2rLbtm0jxcXFZNy4ceTRRx8lTz/9NDn//POJIAjkzTff7DeOcePGkQsuuIAsWLCAzJ8/nxBCyA033EDsdjv5xS9+Qf7+97+TRx99lFx++eXkxRdf1Na/4447yGWXXUYeeeQR8vzzz5NbbrmFWCwWcs0114T8DZYvX06sVis58cQTyZ/+9Cfy0EMPkYqKClJaWqr9DaJx+PBhAoA89dRT/d6L5ixRfv/73xMA5N1339VeA0DGjh1LKisryUMPPUSeeeYZsnnz5oTPQ/o3O+WUU8i3vvUt8tRTT5Gf/exnRBRFcv7558d1XebNmxdy7AkhZPTo0eThhx8mK1asIIIgkNbWVkIIIYqikNLSUnLppZdqyw4bNozceeed5OmnnyZPPvkkOfvss/vt4+9+9zsiiiJpbGwM+exPPvmk37l66623EqvVSmbNmkWee+45cv/99xOXy0XOOuss4vP5Qtanx2LBggUx95GTGbhY4uQUevGL9ONwOEKW/eqrr4jdbie33noraW1tJUOHDiUTJ04kfr9fW4bePIcOHaqJKkIIee211wgA8te//pUQol4ITzjhBHLJJZeEXGC7u7vJqFGjyMUXX6y9RkXJ9ddf32/80cSSw+EIuSA///zzBACpqqoKGVf4xTuVcf3Xf/1XyOdfddVVpLy8POS1aGG4K6+8ktjtdlJXV6e91tDQQAoLC8n555/fb/lIrFu3LuS4nXTSSeTjjz/ut9znn39OAJDly5dr+zps2DByzz339FtWL7FECCGnnXYaKS0t1X4PF0tvvfVW3FAwFUt5eXnk8OHD2usbNmwgAMicOXO016ZOnUpOOeUU0tvbq72mKAqZPHkyOeGEE/qNY8qUKUSSpJDPKy4uJj/72c9i7rvH4+n32iOPPEIEQSAHDhzQXjv99NPJoEGDQsTr1q1biSiK5MYbb4z5GStWrCAAyNKlS/u9F08s0b8r/c4Ron43RFEk27dvD1k20fOQ/s0mTJgQIib+9Kc/EQDk7bffjrk/7733HgFA/v3vfxNCCGlsbCQAyCeffEI6OzuJxWIh7733HiFEFb0AyB//+Edt/b4PW4SoYvzkk08mF110kfbarl27IoqaO++8kxQUFGjbWLVqFQFAFi9eHLIcfQgMf50QQux2O7njjjti7iMnM/AwHIcJnnnmGSxfvjzk5/333w9Z5uSTT8ZDDz2Ev//977jkkkvQ0tKCRYsWwWq19tvejTfeiMLCQu33a665BtXV1fi///s/AGrS6u7du3HDDTfg2LFjaGlpQUtLCzweD6ZOnYpPP/0UiqKEbPOnP/1pwvszdepULRwFQJv5dfXVV4eMi76+d+9e3cb1rW99C8eOHUNHR0fMMcqyjA8//BBXXnklRo8erb1eXV2NG264AatXr467DQAYN24cli9fjv/85z+477774HK5Is6GW7x4MQYPHqyFKARBwA9/+EO88sorkGU5ZNn9+/frNrW/oKAAnZ2dUd8vKSkBALz77rvw+/0xt3XllVdi6NCh2u9nn302Jk2apJ1XbrcbtbW1uPbaa9HZ2akdv2PHjuGSSy7B7t27UV9fH7LNWbNmwWKx9BvThg0b0NDQEHUs+fn52v89Hg9aWlowefJkEEK0GV+NjY3YsmULbrrpJpSVlWnLn3rqqbj44ou1cUfj2LFjAIDS0tKYy0WioKAAAPr97S+44AKMGzdO+z2V8/C2224LSRy/4447YLVa4+7P5MmTIYoiVq9eDUANs9lsNpx11lnaTEIaiqP/0pA7AOTl5Wn/b21tRXt7O771rW+FhEdPPPFEnH766Xj11VdD9vH111/H5Zdfrm1jyZIlKC4uxsUXX6ydJy0tLZgwYQIKCgrw8ccf9xt/aWkpWlpaYu4jJzP0v8twODng7LPPxsSJE+Mu98tf/hKvvPIKNm7ciIcffjjkotuXE044IeR3QRBw/PHHazfg3bt3AwB+8pOfRP2s9vb2kJtEpNl60RgxYkTI78XFxQCA4cOHR3yd5iikMq7wz6Lvtba2oqioKOp2jh49iu7ubpx00kn93hs7diwURcGhQ4cwfvz4qNsAgKKiIkybNg0A8L3vfQ8vvfQSvve972HTpk047bTTAKg3i1deeQXf/va3Q6ZnT5o0CU888QQ++ugjTJ8+PebnpEpXV1eIQA3nggsuwNVXX42HHnoIf/7zn3HhhRfiyiuvxA033ACHwxGybPh5Bag3x9deew0AsGfPHhBC8Otf/xq//vWvI35ec3NziOCKdF796U9/wk9+8hMMHz4cEyZMwGWXXYYbb7wxREwcPHgQ/+///T+88847/XJcaJ4QrYsU7Rh/8MEH8Hg8cLlcEcdKISlMmqaCOfxvH76/qZyH4cehoKAA1dXV2ve7vb09JHfLbrejrKwMJSUlGD9+fIggOuOMMzQBM3ny5JD37HY7zj77bG077777Lv7whz9gy5Yt8Hq92uvh+Vw//OEP8d///d+or6/H0KFDsXLlSjQ3N+OHP/yhtszu3bvR3t6OQYMGRfrzobm5ud9rhBBDz+A0MlwscQzF3r17NUFBE6ZTgbozjz32GE4//fSIy9AnY0rfp8p4hDsF8V6nN6NUxhVvm9nm+9//Pn784x/jlVde0cRSbW0tGhsb8corr+CVV17pt87ixYszIpb8fj+++eYbLUE3EoIg4PXXX8f69euxdOlSfPDBB/iv//ovPPHEE1i/fn2/v3cs6PGbO3cuLrnkkojLhJdKiHReXXvttfjWt76Ft956Cx9++CEee+wxPProo3jzzTdx6aWXQpZlXHzxxXC73bj//vsxZswYuFwu1NfX46abburnPqZKeXk5AMRNno4ETXxPZH/15p577sGiRYu03y+44AKsXLkSgOoUPffcc2hra8OaNWswefJkbbnJkyfjn//8J/x+P1avXo0JEyZoZUFWrVqFK664Aueffz6effZZVFdXw2azYeHChXjppZdCPv+HP/wh5s2bhyVLluDnP/85XnvtNRQXF2PGjBnaMoqiYNCgQVEnOVRWVvZ7ra2tjelSKmaGiyWOYVAUBTfddBOKiorw85//HA8//DCuueYafP/73++3LBVUFEII9uzZoxXsO+644wCEuiIskKlxRXoaraysRH5+Pnbt2tXvvZ07d0IUxX5OWCJ4vV4oihIyC2rx4sUYNGgQnnnmmX7Lv/nmm3jrrbfw3HPP6X4jff3119HT0xNVuPTlnHPOwTnnnIM//vGPeOmllzBz5ky88soruPXWW7Vlws8rAPjmm2+0kCt1fmw2W9rHr7q6GnfeeSfuvPNONDc348wzz8Qf//hHXHrppfjqq6/wzTffYNGiRbjxxhu1dcKLQdIaTNGOcUVFRUxXacyYMQDQr1hjPGRZxksvvYT8/PyQMFYkUjkPd+/eHTLjrKurC42NjbjssssAAPfdd19IvaS+TuyUKVPwP//zP1ixYgU2b96MX/7yl9p7kydPRk9PD9577z3s3bsXV199tfbeG2+8AafTiQ8++CDEcVy4cGG/cY8aNQpnn302Xn31VcyePRtvvvkmrrzyypD1jjvuOKxYsQLnnXdeQud9fX09fD4fxo4dG3dZjv7wnCWOYXjyySexdu1avPDCC/j973+PyZMn44477ogYw//Xv/4Vkivx+uuvo7GxEZdeeikAYMKECTjuuOPw+OOPR8yvyVXl50yNy+Vy9ZvKb7FYMH36dLz99tsh+UFHjhzBSy+9hClTpsQM47W1tUXM8fn73/8OAFpYtaenB2+++Sa++93v4pprrun3M3v2bHR2doZMrdejdMDWrVvx85//HKWlpfjZz34WdbnW1tZ+Lhx19fqGWgB1Gn3fnKONGzdiw4YN2nk1aNAgXHjhhXj++efR2NjY77MSOX6yLIcITbrdIUOGaOOhbmLfcRNC8Ne//jVkverqapx++ulYtGhRyPHftm0bPvzwQ01cRGPo0KEYPnw4Pv/887jj7jv+u+++Gzt27MDdd98d8xyi+5LsefjCCy+EnHv/8z//A0mStOMwbtw4TJs2TfuZMGGCtiwVb08++ST8fn+Is1RTU4Pq6mr86U9/ClmWjlMQhJD8uv3790etjP/DH/4Q69evxz//+U+0tLSEhOAA1T2UZRm///3v+60rSVK/7+sXX3wBACHj9fv92LlzZ8RzjaMv3FniMMH777+PnTt39nt98uTJGD16NHbs2IFf//rXuOmmm3D55ZcDUGuunH766bjzzju1nBFKWVkZpkyZgptvvhlHjhzBX/7yFxx//PGYNWsWAEAURfz973/HpZdeivHjx+Pmm2/G0KFDUV9fj48//hhFRUVYunRp5nc8jEyNa8KECVixYgWefPJJDBkyBKNGjcKkSZPwhz/8AcuXL8eUKVNw5513wmq14vnnn4fX69VuGNFYuXIl7r77blxzzTU44YQT4PP5sGrVKrz55puYOHGi9mT/zjvvoLOzE1dccUXE7ZxzzjmorKzE4sWLtRvK1KlTASDhJO9Vq1aht7cXsizj2LFjWLNmDd555x0UFxfjrbfeQlVVVdR1Fy1ahGeffRZXXXUVjjvuOHR2duJvf/sbioqK+omJ448/HlOmTMEdd9wBr9eLv/zlLygvLw/p2/XMM89gypQpOOWUUzBr1iyMHj0aR44cwbp163D48GGtJlg0Ojs7MWzYMFxzzTU47bTTUFBQgBUrVuCzzz7DE088AUB1fI477jjMnTsX9fX1KCoqwhtvvBExXPbYY4/h0ksvxbnnnotbbrkFPT09WLBgAYqLi/Hb3/427t/2e9/7Ht56662I+TLt7e148cUXAQDd3d1aBe+6ujpcd911EYVAJJI9D30+H6ZOnYprr70Wu3btwrPPPospU6ZEPcf6MmLECAwfPhzr1q1DTU0NhgwZEvL+5MmT8cYbb0AQBJx33nna69/5znfw5JNPYsaMGbjhhhvQ3NyMZ555Bscffzy+/PLLfp9z7bXXYu7cuZg7dy7Kysr6OY0XXHABbr/9djzyyCPYsmULpk+fDpvNht27d2PJkiX461//GlKTafny5RgxYgTOOOMM7bX6+nqMHTsWP/nJT3Svzs8JI0ez8DgcQkjs0gEAyMKFC4kkSeSss84iw4YNI21tbSHr//WvfyUAyKuvvkoICU4lf/nll8m8efPIoEGDSF5eHvnOd74TMp2asnnzZvL973+flJeXE4fDQUaOHEmuvfZa8tFHH2nL0Cn6R48e7bd+tNIB4dO+6dTzxx57LOT1aFPf0xlX+NR4QgjZuXMnOf/880leXh4BEFJGYNOmTeSSSy4hBQUFJD8/n3z729/W6jDFYs+ePeTGG28ko0ePJnl5ecTpdJLx48eT3/zmNyGVqC+//HLidDojTnWn3HTTTcRms2m1tZItHUB/bDYbqaysJOeffz754x//SJqbm/utE/732bRpE7n++uvJiBEjiMPhIIMGDSLf/e53yeeff66t0/f4PfHEE2T48OFa7apI1Z3r6urIjTfeSKqqqojNZiNDhw4l3/3ud8nrr7/ebxzhJQu8Xi/55S9/SU477TRSWFhIXC4XOe2008izzz4bstzXX39Npk2bRgoKCkhFRQWZNWsW2bp1q/a96cuKFSvIeeedR/Ly8khRURG5/PLLyddffx3370v/PgDIqlWrQl6/4IILQv72BQUF5IQTTiA/+tGPyIcffhhxW5G+G30/J955SP9mn3zyCbnttttIaWkpKSgoIDNnzoxY1ysa119/PQFAbrjhhn7vPfnkk1o9qHD+8Y9/kBNOOIE4HA4yZswYsnDhwojXAMp5551HAJBbb7016lheeOEFMmHCBJKXl0cKCwvJKaecQu67776QqvOyLJPq6mryq1/9KmRdel5mqzr/QIb3huOYCtorbMmSJSFPZRxOOuzfvx+jRo3CY489hrlz5+Z6OFln6tSpGDJkCP7973/ndBy0l+Rnn32W0OxZs/Cf//wHN9xwA+rq6lBdXZ3r4QxIeM4Sh8PhcGLy8MMP49VXX9VKEXCyy6OPPorZs2dzoZRDeM4Sh8PhcGIyadIk+Hy+XA9jwLJu3bpcD2HAw50lDofD4XA4nBjwnCUOh8PhcDicGHBnicPhcDgcDicGXCxxOBwOh8PhxMAwCd5utxt33XUXli5dClEUcfXVV+Ovf/1rzL5NdXV1mDt3LlavXg2v14sZM2ZgwYIFGDx4sLbMFVdcgS1btqC5uRmlpaWYNm0aHn300X6FymKhKAoaGhpQWFjImxxyOBwOh2MQCCHo7OzEkCFDIIrR/SPD5CxdeumlaGxsxPPPPw+/34+bb74ZZ511Vr8GhhSPx4NTTz0Vp512Gh566CEAwK9//Ws0NDRg/fr12h/lz3/+M84991xUV1ejvr5eq6Gydu3ahMd2+PDhlHpocTgcDofDyT2HDh3CsGHDor5vCLG0Y8cOjBs3LqQQ2bJly3DZZZfh8OHDEV2gDz/8EJdeeilaW1u1vkLt7e0oLS3Fhx9+GLXJ5TvvvIMrr7wSXq8XNpstofG1t7ejpKQEhw4ditsHicPhcDgcDht0dHRg+PDhaGtrQ3FxcdTlDBGGW7duHUpKSkIqtk6bNg2iKGLDhg246qqr+q3j9XohCEJIl2en0wlRFLF69eqIYsntdmPx4sWYPHlyTKHk9XpDGmzShq1FRUVcLHE4HA6HYzDipdAYIsG7qakJgwYNCnnNarWirKwMTU1NEdc555xz4HK5cP/996O7uxsejwdz586FLMv9OjTff//9cLlcKC8vx8GDB/H222/HHM8jjzyC4uJi7YeH4DgcDofDMS85FUsPPPAABEGI+ROpE30iVFZWYsmSJVi6dCkKCgpQXFyMtrY2nHnmmf2SuH75y19i8+bN+PDDD2GxWHDjjTciVnRy3rx5aG9v134OHTqU0hg5HA6Hw+GwT07DcPfeey9uuummmMuMHj0aVVVVaG5uDnldkiS43W5UVVVFXXf69Omoq6tDS0sLrFYrSkpKUFVVhdGjR4csV1FRgYqKCpx44okYO3Yshg8fjvXr1+Pcc8+NuF2HwxES3uNwOBwOh2NeciqWKisrUVlZGXe5c889F21tbfjiiy8wYcIEAEBtbS0URcGkSZPirl9RUaGt09zcjCuuuCLqsoqiAEBIThKHw+FwOJyBiyFylsaOHYsZM2Zg1qxZ2LhxI9asWYPZs2fjuuuu02bC1dfXY8yYMdi4caO23sKFC7F+/XrU1dXhxRdfxA9+8APMmTMHJ510EgBgw4YNePrpp7FlyxYcOHAAtbW1uP7663HcccdFdZU4HA6Hw+EMLAwxGw4AFi9ejNmzZ2Pq1KlaUcqnnnpKe9/v92PXrl3o7u7WXtu1axfmzZsHt9uNmpoaPPjgg5gzZ472fn5+Pt5880385je/gcfjQXV1NWbMmIFf/epXPMzG4XA4HA4HgEHqLLFOR0cHiouL0d7ezksHcDgcDodjEBK9fxsiDMfhcDgcDoeTK7hY4nA4HA6Hw4kBF0scDofD4XA4MeBiicPhcDgcDicGXCxxOBwOh8PhxICLJQ6Hw+FwOJwYGKbOklFpbm7GJ598ErPXXDIsX74cbW1tKCkpwcUXX6zLNgHA5XLhkksugdXKTwkOh8PhcPrC74wZ5h//+Ac++OAD3bd79OhR7N69W9dtlpaWYvLkybpuk8PhcDgco8PFUoY5cOAAIFrQfdxFumzPeXA9RG8nFEchekeco8s2Ld3H4KjfhEOHDumyPQ6Hw+FwzAQXSxmmvr4esqMQcslwXbZH6jep/1rs+m3TUQBH/SbU19frsj0Oh8PhcMwET/DOIJ2dnejs7ITiYLsFiuIoBAA0NDTkeCQcDofD4bAHF0sZpKmpCYDq3DCNaAWx5aGp6UiuR8LhcDgcDnNwsZRBjh49CgBQ7K4cjyQ+it2F5uZm3WbtcTgcDodjFrhYyiDNzc0AAGIQseTzedHe3p7roXA4HA6HwxRcLGWQlpYWAACxsS+W6BjpmDkcDofD4ahwsZRB2traAACKLS+3A0kAYnMCCI6Zw+FwOByOChdLGaS1tRVAUIiwDAkIOi6WOBwOh8MJhYulDNLW1gYIIiDacj2UuBCrKuiowONwOBwOh6PCxVIG6erqArE6AEHI9VDiQix2AIDH48nxSDgcDofDYQsuljJIV1cXiIV9VwkAiJWLJQ6Hw+FwIsHbnWQQj8cDYkm/IGX+trcg+HsBAIKk/it2u+Ha/DKIzYnuk69K+zNy5SzdfPPNUfOkSkpKsHDhwqyOR2/Mvn8cDoczEOBiKUMQQtDb2wtSWJL2tgR/L0SpJ/Q1EAhSD5S0tx5AVE+Fnp6eOAvqS1tbm6nzpMy+fxwOhzMQ4GG4DCFJkvof0ZLbgSQICYglv9+f45FwOBwOh8MW3FnKED6fDwBABH3E0rPPPhvx9Z/efa8u24eg6mY67lRpb2/HrbfeCneCbopMRWUEWltbMXXatKQ+f9iwYfj73/4Gmy07uWINDQ04cOBA1PdjiU+/349169ZFfb+6uho1NTXpDE93duzYkXThUrvdjjPOOAN2uz1Do9KfI0eOxDx2paWlcLnYLzYbi+bm5pB9dLlcKCkpyd2AdMbr9aKnpwd5eXlwOBy5Ho6ueDwetLa2wuVyobS0NNfD0RWPx6OZDQUFBbBY2DAcuFjKENpFSDCIeRcYZ7rOUmNjI44ePQrFlg/FURh3eYvnKAQSOZhIBBFeZ3nCny32tuPA/v3o7OxEWVlZwuulw+zZs+F2u1Nat6urC/PmzYv6vs1mw9KlS+F0slGnq7m5GXfccUdK686ePRvXXHONziPKDO+//z4effTRmMsUF5fgjTdeh9VqzEvoe++9h8ceeyzkNUEU8Y+//x2jR4/O0aj0o7OzE9dddx08Hg9cLhdeeeUVFBbGvx4ZAb/fj+uvvx4dHR2mOmYAsHLlSjz00ENaj9KTTz4FTz+9IMejUjHmN91I6FQ24M4774z8hlWn6uA6jZOe5P7y4+EbPjHu8q7NL0OQIudJEYsDPWO/k/BnO+tWQnTvzWoz4M7OTij2AvgGj4v4vqN+MwQlsgAlog3eoWdEfM/WsgfoccPr9TIjlmjyv1RYBam0JqF1xN4O2Ju/RldXVwZHpi9NTU0AAH/JCK1Ya1+s7fVob2+D1+s1rFgK30dLtxsWz1EcPXrUFDdet9utna8ejwdut9s0Yqm3txcdHR0AAKIopjlmAHDo0CEQQiAVVsHSfQwHD0Z37bONMb/pHE5Esl/PihACxZEPf9XJEd+3N34VQyxZo65n6ToKS487q8IvUZS8MvijiMNwLJ1NsDd/zeR+REMT/FWnQC4c3O99YfdHEH1dhtqncOjYfdWnQikYBFvTV7B4jhp6nzjmwTfkdDgObgAg53ooGgaJERkYg1179LtY5mLHDfbHjgX7dUxNC/0ORD2bAi6soug2F5UZzCqWBAMUBk6U8H0x876xVNCZi6UMEUxKM8gFNZA3lG5Yge63kIuLbuAzs5kQKAhCZnRNYF/MciE05H7EGbMh9ykOZtkns+xHJAaEWKLXP4aeGrlYyhB05o9glKdPRbU7052xJIr0lMqds5RdsSRqX+xMEPx75p6Ujm3gb8PSfnD6QM9dkxlK4eebmc6/ASGWAIAQiBZ2jhs7IzEZ2tR1wk7MNSYBZyndKfeaUIkywy2jBD4zm2JJ/TJn4E7DsrNk0lDNQCLaecXk+aYDZtovM4ul0AcyApGhfeNiKUOIogibzQZBjl5HiCUERR1nuvVIcikShcBnZqvGEhAQZhkQD0IgfMvSE3FQhCbjLLG3H/GgN5/ooWTji0V6PAQT7Eskws83cwqKyL8bmb5hOIEQpvaNnZGYkPx8F6CkV+QxWwiyOs50C+3lNPyo6JN3lQxWizUzLppOTp+eaBeupMRh9kOj6RK8sUbZTxOEFvu5EwY8TrEI3z8jH6twzCyWgpGJgLPE0L6xMxITUlDggiAbpH1IYJx6iaWcOEuKrLp5WXyKtFotGRVLLNXx0ZL3U8hZMtJNONELtJHdin7Cl+WwbwqEn28s3XTTxcxiKeh4KgAhTF03zPNXZhCXywVRNpazlJ+fn9Z2aBiPhvWyiiJlvYCjzWaDoGRAGCoKLBYLUzcv7cKVjGtoQBcmfEZOv/dNICz6u2fG36e+cGfJmHBnaYBSXFysOjaZuJnqjODvBYC0e0NpOU85yNUSFAnOPJ0qmieI3W6P2q4lHQQiw8ZYLzXN5Upif3ORR5YuQdcl2n4azy0LJ5qzxNLNKR3Cj42Rj1U4giCEHCcz7VtfsSQQhSln3RzfDEahwkOQenM7kASgY0xXLImiCLvdkRNnSVAkOLPcMNNut2cm5KjIcNjZav6ZilhiMZwYj7iJ7AZMWg+n/6xV4wvAvpjZfQFCj5NZjhkQdl4Shal9M9cZxBi0GzR1bVhG8Kv92fToOu5y5Ws5UNlEUPxZ7wRvt9szEoYTFBl2O1tuDHWHknLSDCiW4iayGzAPK5x+gtAEArAvZs5ZAswrlvo+kAk8Z2ngQDvfC/7uHI8kPmJALFGBlw4ul0vLgcoaRAFkf9o5V8nidDoBRdK9fICQg/yreKRSFoIKSSOF4YJV6KOIQqJAFEVD5/eEV9oXTCAA+xK+H0YS64lgVrEU8t3jzlJquN1uzJw5E0VFRSgpKcEtt9wSt5N5XV0drrrqKlRWVqKoqAjXXnstjhw5EnFZr9eL008/HYIgYMuWLbqMubKyEgAg+jy6bC+TCD4PBEHQxpwOLpcLYpTmsRlDp9l8yaLlaOkcdmRRLImiqF68knHSiD6V4bNJvHCjQAgsBr/59gvDGdABjIWZc5YAhJx/ZjlmQN8HMlUssfSQZRixNHPmTGzfvh3Lly/Hu+++i08//RS33XZb1OU9Hg+mT58OQRBQW1uLNWvWwOfz4fLLL4/YAPO+++7DkCFDdB0zFR7GcJY8KC0t0+WLV1BQoIqXLFbx1qtOVLJQQaN7jhaDYgkAbHa7lrSdCEZ0luLmZjH2xJsK/cWSuRO8zbJfFKsleJ02+rnYl/CcJZaEIDsjicGOHTuwbNkyfPbZZ5g4cSIAYMGCBbjsssvw+OOPRxQ5a9aswf79+7F582YUFRUBABYtWoTS0lLU1tZi2rRp2rLvv/8+PvzwQ7zxxht4//33dRu35ix5YztgOYcQiL5uDKo5XpfNFRYWAgAEyQdiy84NX5C8AKAd62yRR2ffyX7AptNMPEUGiBLcNkPY7XZ4fEk4Szr1HMwmcVv2EBk2hi7iqdBfLBlP1MbC/M5ScH9YEhTpouVFBh4+Wdo3Q8jtdevWoaSkRBNKADBt2jSIoogNGzZEXMfr9UIQhJD2HU6nE6IoYvXq1dprR44cwaxZs/Dvf/874XwXr9eLjo6OkJ9IVFZWqrVyvJ0JbTdXCP4eQJFQXV2ty/aoWILs1WV7iUCdpWyLJXrOCDqGHWkh02znXyWC05HkTEed2uhkE60KfQxnyWYzjviLRDBZPzTBm6WbUzoIgqAJJIvVauj8skjYTBqG0/YlcA1kSbwbQiw1NTVh0KBBIa9ZrVaUlZWhqakp4jrnnHMOXC4X7r//fnR3d8Pj8WDu3LmQZRmNjY0AAEIIbrrpJvz0pz8NEWLxeOSRR1BcXKz9DB8+POJyVqsVgwcPhsXHtlgSA2JOrzAkFSzU7ckGtPSBJtSyhCaW9Jz9FxBeLDpLDocjqdl/dFkjiaX4OUsKbDZj36DC91EwmVgCgm6SRTSXqwSYN8GbO0tReOCBByAIQsyfnTt3prTtyspKLFmyBEuXLkVBQQGKi4vR1taGM888U4tfL1iwAJ2dnZg3b15S2543bx7a29u1n0OHDkVddsiQIRB83bonAOuJ4FWdMb2cJa2+VBZLJuhVVDNZNPdHR7GUq/yrRHA4HAAxt7OkPc1GE4WMJZ6mQlAsBfbRlGJJ3Rer1TxigmI1ubNEHz5Z2recjuTee+/FTTfdFHOZ0aNHo6qqCs3NzSGvS5IEt9uNqqqqqOtOnz4ddXV1aGlpgdVqRUlJCaqqqjB69GgAQG1tLdatW9fvQj5x4kTMnDkTixYtirhdh8OR8MV/2LBh+PzzzyH2dkDJL0tonWwj9rYDAEaMGKHL9oqLi9XtSr3IVu1yvYpqJktBQYH6+TqWShByNLMvEZxOJ4QkqrPTJ0QWXbJoBENUkc9e2oPQyAQFoRLyr9H3qy9UJLF0w9WLvsfJTMdM2xcGw3A5PYsqKysTmqp+7rnnoq2tDV988QUmTJgAQBU6iqJg0qRJcdevqKjQ1mlubsYVV1wBAHjqqafwhz/8QVuuoaEBl1xyCV599dWEtpsIVICIvW0MiyXVWYoWTkyWoLPUo8v2EiFXzlImxBIC26LbZgmn06nNVIGQgDEdEEsszuyLRj8hEYZAzCOWhLAwnNH3qy9UJBm9zEMkzB+G84X8zgKGOIvGjh2LGTNmYNasWXjuuefg9/sxe/ZsXHfddVqeTX19PaZOnYp//etfOPvsswEACxcuxNixY1FZWYl169bhnnvuwZw5c3DSSScB6O+k0JvTcccdh2HDhukydk0s9bTrsr1MIPa2oaCgUDehoRXjlLIplnpCPjtbaGJJ0tFZktgVSyGz/6zx3VXBgGJJm7kXLQynyIaa3ReJ/mE4OfR1E0D3xUz7RNGEIGPNttNFE0sBZ4ml75lhzqLFixdj9uzZmDp1KkRRxNVXX42nnnpKe9/v92PXrl3o7g7WNNq1axfmzZsHt9uNmpoaPPjgg5gzZ05Wxz1y5EgAgNjTltXPTRhFgaW3AzXjx+n2pQtWLs+eWBL9PbBarVkXGBkJwwUS47OdrJ4IVCwJigSCBMSS7Ifd7jDU029wNlwEsUQIQBSmLuKp0C8vS5GDRUdNAhUURi/zEAl6/CwWc+1bMAwnhf7OAIb5S5eVleGll16K+n5NTQ1IWMuJ+fPnY/78+Ql/RqRtpEtFRQUKCgrR3tOq63b1QuxtB4iCUaNG6bbN4uJiCKKY3TCc1I2ysrKsP2VppQp0nPknyLmpGZUIQWcpwbwlRTJUvhIQx1kyYEXySEQKw7F0Y9KDvu6L2dDagpjHVALQ11liLwxniNIBRkYQBBx33GhYvO3JtYnIEmKPGwC0pHc9sFgsKCsthejLUuVyQiD6e7TctGyiFeDUsaaUEcJwidaVEmQ/8vONKZYiOksGLLIZiX7OEpENXzsqHCoozByGMxvad4/BBG8ulrLAqFGj1Bs6g6E4MeB46eksAWryvujv1r3BbERkL6DIORFLLpdLddH0dJYCM/vorEKWSLaulKBkv7lxusTq92fEulGR0MQeCYbhjC4AwxkIOUtms5aCCd7s5SxxsZQFjj9ebSMidh/L8Uj6Y/GoY6Jj1IuKigr1ZqPnLLEoUAcrF2JJFEUUFhZqAkcPctW6JRGSqitFSMBZMqpYiuQsGa9uVCS0J3iFhuFk2O3sPMXrwUAQS+aSSup+CX36+HFnaYBx4oknAgAsrIklQmDpPoYhQ4boHvLR+uL5PLpuNxJC4DMSKUORCUqKiyHq7Cw58/KYulBQaO2nhBLaidrjzmhiKSgkIjlL5hJL1FkSFJ6zZCTMKAApNiubNaS4WMoCNTU1sFitmovDCoK/G4LUq4k5PaHtaYQsiCUqyMJb4mSL4uJi1Q3SKeQoSF6UMBiCA/qG4eKLJZaLa8bCarWqtXkiVd03WRhO6JOzZPR9CofnLBkTm52LpQGL3W7H6FGjYOlxR+9kngMsnhYAwAknnKD7tqlwyaazlCuxVFJSoh5XnUKOotSb9eKaiRJ0lhIIwzHctiUezig98IxYNyoS2k1Ic5bMl7NE21qJovluc2YWS/Y+Aomlc9J8ZxGjjBkzBlAkLaGaBUTPUQBq0U+9GTx4MABA8HXpvu1wxMBn5FQsAfrkLckSoEjMiiUtXGtiZwkAHA5nFGdJfY2li3gqiKKo3nAVSXVETdDCJRwzFWsMx4yhRQqrrVy4WMoSVJBQN4cFLJ4WCIKQkTAcFUuiN/NiSfB2wWKx5CTBGwiKJVGHxsG56nGXKMnkLAkMt22Jh9PpiJmzZLTaUZGw2+1qgjchAIjpwnAUM4omMztLfc9DLpYGIGPGjAEAiF1HczySAITA4mnB8OHDM3IzKy8vh8VqzZqzVDloUM6etkpLSwHo096FFvKk22SNpMSSZOAwnNMZMQxnltlwQMAdI5L6A+O7ZQOJgeIssXROcrGUJUaOHIm8vHxYPGyIJbG3HYLsw/jx4zOzfVHE4EGDYMm0WFIUiL5uVAWcrFwQbBw8gJylBHrhGdtZckZxlmTtfaOjOkuytk8s3Zg4sRkoYok7SwMQi8WCsWPHwNLTCujYdDVVxK5mAMC4ceMy9hlDhgyB4OuOnPuhE6pzRbSGyrlAc5Z0aO/CurPkcDjUm2wiyeyBquZGdZa0fJ6+BIrlmcFZcjgc6uw+k4slvVtYsQBNWjfjvnFniaO5OCy4S5YsiKXq6moAgODN3Iw40dsJAKiqqsrYZ8RDT7EkBrZBmxGzSEFBQWIJ3oGHAhYbAscjWhVvQTbHbDgg4CwRRZsRZwYBGAkz5ixRzLhvfQUSd5YGKJpYCgiVXGLpakZ+fj5qamoy9hlULInejox9BhVL9LNywUDKWQJU8SMmkeBtRLFExVC/UJzpErwlbR9ZeornDFz6Jq9zsTRAGT9+PARBgKXrSG4HInlh6W3D+PHjMxr7pqExKmgyAd12LsNwRUVFsFitmiuUDlRwsSyWCgoKEirCSdu2GDVnCUB/Z8lsCd6KxMNwBsaM+8ZzljgoLCxETU0NrJ6jOS1OScXaKaecktHPyYZYEgKu1dChQzP2GXHHIAgoKy3VLWdJFEUmm+hSCgsL1fM30myxPpjZWTJDGI4KPloPywwCsC9mFBLhmDEMx8USBwBw8sknA7IfYnfuilNaOo8Ex5JBgmIpg2G43k7k5eXnXFyUlZXp4yz5e1BSUsr0bBcqfgQ5dj88QfLCZrMZ8iasiSE5srNkBrGktTwJHEezOUtUSJhRUFAhaEZB2DcMx1I9KS6Wssypp54KALB0NeVsDNauI7BYLBlN7gbU8EtRcTGE3gw5S4TA4u3AsGFDc35BLCsrU12HRNqAxED096CsjN0QHKCGHYFgmC0qss+QrhIQzEkKd5bMluANBBPxzSaWKGYUFLm+3mUS7ixxAPQRS505yltSJFg8LTjxxBOzcsEfNnQoLN6OjIQdBakHUKSchuAodPaa4O9OfSOKBEH2oby8XKdRZQaagxSvfIAoeTVhZTSi5SyZqShlMAxnbrFkZmFhxn3jzhIHgNoGpLJyEKxdR3TrUp8MFk8LQBRNtGWaoUOHAkRR6y3pjNirhveGDRum+7aThQqcdEJxNOeJdbGUkLNECAQDO0uakIiQ4O1wOEzRnFUTRyYXSxxj0ddN4mJpgHPaaadC8PdoycnZxNLZFBjDaVn5POr6ZCJvSejNfXI3hQqcdJwlKihZrrEE9EnYjiWWFAkgimHFklYaIIKz5DBBCA7gYTgOm/QVSCzlbnKxlAOoULF2Zj9vydLZBEEQMj4TjjJ8+HAAansVvREZEktaGM6XurNkhIKUQGIJ3tR1MmoYLnymGEVQ/Mgzm1gyubNkxlCVmWHJTeoLF0s5gIolS7bFkqLA2tWMUaNGZe2JX3OWevV3lqhbRQVZLtHFWQqsa4YwnNHFUjBnKbQ8gqDIpihICZh/NpyZZ4yZGS6WOBrDhw9HSUlJ1pO8xe4WQJGylq8EZNpZakd+fj4TTWepG6RHzpJhnKVYYkk2bkFKoG+dpXBnSTJFcjcwcMJwHGPBxRJHQxAEnHbaaRB9XRC8XVn7XCrOTj/99Kx9psvlQmlpqf7OEiGweDsxfPhwJmz24Gy4dMJwRnOWos+Go0Iq1/WvUiUolvo4S4QAsmSKsgEAT/A2A2Z0zVjKU+oLF0s5IhehOJojlU1nCVDdJdHXBSj6lQ8QfB5AkZgIwQHqjaawsHBAhOGCpQPih+GMmuAdsXQAUQAQ04glOuvI7DlLZoaFB0W94c4SJ4SsiyWiwNJ1BMOGDct6mGfYsGGB8gH6Faek+UoslA2glJeXpxeG8/XA5XIxH+axWq3Iz88fEDlLIaUDTFS9G+ibs+QP+Z3DySXcWeKEMGrUKLgKCrI2I07saYUg+7IagqOMGDFCHYOOeUt0W6w4S4AqlgSpN27PtGiIUg/zrhKluLjY1DlLmmDt0+7ETE10gT7OUmC/WKqWzEkMM4bhuLPECUEURZx26qkQvR0ZKdgYjiVHITgg6P7ombfEolhKK2+JKBD8xhFLhYWFEGNV8DZLzhIxv7NE4WLJeJgxDMedJU4/shmKo8nd2SpG2ZdMzIhjqXo3JVg+IHmxJPh7AbA/E45SVFSkJgZHyUMzes6SJiSU/s6SWcRSuDjiYonDAlwscfoRbKqb4RIChMDa2YTBgwdj8ODBmf2sCFRXV0MQRd3DcOXl5cjPz9dtm+mSjrNklORuSrzClILsgyiKcLlc2RyWboiiCLvdrjXOBaCFV82S2xMujsyyXxxjw8USpx8nnHACnE5nxp0lsbcdgtSbE1cJUC/CVYMHw6JXGE6RIXq7mArBAX37wyUfVjVKjSWKlrgdpXyAIHlRUFho6DCBw+EIyT/jzhKHk3m4WOL0w2q14uSTT4alpzV2n600yWW+EmXEiBGqexLWPiIVRG8nAMKcWErPWTKWWIrnLImSF8UGnQlHcTgcEXOWzJLg3ddJslgspmgOzDE+rJ6HbI5qAJGNUBzddi7FUjDJO/1QHN0GS/lKQHpiySh94SjBwpS9Ed8XZK9hZ8JRojlLZglXsdrdnTOw4WKJExHa0DaTrU8sXUdQXFySUycmmOSdfihO6GWnJ1xfBqSzFCkMp0iAIhu2xhLF4XCEVvAO/N8szlJfsWSzmUMAcowPD8NxIjJ27FhYrFZYM+QsCT4PRG8XTj31lJzmj2TCWWJNLBUUFMBqtaZUmJImeBtPLPUPwxl9JhxFFUt9Z8OZWCzZeb4Shw24s8SJiNPpxEknngiLpyW0tYJOUMeKOli5Qs/yAaK3A6Ioorq6Ou1t6YkgCCgtK4MgpZDgLfVCFEXDuDFaGC5CzhJ1m4wulux2u+om0cJ/Jp4NZzNhGM7IkwsSxYxFKVk9blwsMcDJJ5+stiPxtOi+bZqvdPLJJ+u+7WSorKyE3W7X2pSkg6W3HdVDhjCZZ1FWWgrRHzmPJxaCvwclJSXMPlWFo/WHixCGowLKKMIvGqooIppYosneZnGW+n5/+Ew4Y8KqsDAjxrgymxwqZCxdzbpv29LVDLvdjhNOOEH3bSeDKIoYOnSomrOUztOQ7IPg78FwxpK7KaWlpapDmOSsP1HqUdc1CLFmw5klDKc5SCSQtxQowGkWZ4mLJQ4ncbhYYoCgWNI5b0n2w9LjxtixY5m4GA4fPhyC7Is6gyoRaIL40KFD9RqWrlDBk1SStyJDkHzGFEuRSl7IJgrDIZirRMNwLHyX9IDPhuOwCKtumWHEktvtxsyZM1FUVISSkhLccsst6OrqirlOXV0drrrqKlRWVqKoqAjXXnstjhwJFSQ1NTUQBCHkZ/78+ZnclX6UlZWhuroalq6j6bkuYVg8LQAhGD9+vG7bTAcqcIQ0ZsSJjM6Eo2hiKQlBSJctKSnJxJAygsPhgNVqhRChP5xZnSWBmEss9Z11xMUShxMbw4ilmTNnYvv27Vi+fDneffddfPrpp7jtttuiLu/xeDB9+nQIgoDa2lqsWbMGPp8Pl19+OZSwfla/+93v0NjYqP3cddddmd6dfowfPx6C1AvB26nbNmlYjxWxpM2ISyNvia7LWo0lSkpiyWB94QD16a+wsDBiBW8qoIxeZ0kTRWHOklnCcANFLLHqVHAiw2rSuiG+ITt27MCyZcvw2WefYeLEiQCABQsW4LLLLsPjjz+OIUOG9FtnzZo12L9/PzZv3qwlmi5atAilpaWora3FtGnTtGULCwtRVVWVnZ2Jwvjx47FixQpYupohOfVJjKViady4cbpsL12os5ROrSXWw3DFxcUAANHfCznOshRB6glZ1ygUFhbC3dV/UoJZZsNRsSQQGQTQHCazOEuCIMBqtUKSJFOLJQ5HDwzhLK1btw4lJSWaUAKAadOmQRRFbNiwIeI6Xq8XgiCEzFxxOp0QRRGrV68OWXb+/PkoLy/HGWecgcceewySFHsKv9frRUdHR8hPulD3x+LRKcmbEFg8R1FdXc1MLoxezpLFasWgQYP0GpaupJKzRJ0lI4XhANU5ipjgHXjNPM6S6kQLJkvwBgAx4C6ZWSyx6lRwjIUhxFJTU1O/m6PVakVZWRmamiI3oT3nnHPgcrlw//33o7u7Gx6PB3PnzoUsy2hsbNSWu/vuu/HKK6/g448/xu23346HH34Y9913X8zxPPLIIyguLtZ+9MifGT16NGx2OywRntRTQfB1QZB6mXGVALXRrN3hgNibeqhR9Haiuqqa2Sqv1B0ye84SEHCOFDmkJQgQdJaMLpb6zYYzmbMEQMuRNLNY4mE4jh7kVCw98MAD/ZKrw3927tyZ0rYrKyuxZMkSLF26FAUFBSguLkZbWxvOPPPMkFo2v/jFL3DhhRfi1FNPxU9/+lM88cQTWLBgAbze6I1t582bh/b2du3n0KFDKY2xL1arVS1O2ePWpTilpesoALVCOCsIgoBhQ4em7izJfgj+HgwbxmYIDggKnoizxKJgVLGk1VoKT/KWfbBYLHA6nTkYlX5QASGQQI5j4F8zCgtWHz7SgTpK3Fni6EFOv/X33nsvbrrpppjLjB49GlVVVWhuDg1PSZIEt9sdM9do+vTpqKurQ0tLC6xWK0pKSlBVVYXRo0dHXWfSpEmQJAn79+/HSSedFHEZh8ORkcJ0Y8aMwbZt2yB2u6EUpBdmsniOattkiSFDhmDv3r2A5AWsyf0NqciKlKPGCqk5S96QdY2C5hxJPsCWp70uyD64XC7DP9GHJ3gLXCxxOAOWnH7rKysrUVlZGXe5c889F21tbfjiiy8wYcIEAEBtbS0URcGkSZPirl9RUaGt09zcjCuuuCLqslu2bIEoijnJiaHCxuJpSVssid3HIIpizotRhkOFjtjbAaUg/rHvCw3fsSyWHA4H7HYH5BTCcEareO1yuQCo4qjvs7sg+1BQaizhFwlNQGjOkgnDcAG4WOJwYmOInKWxY8dixowZmDVrFjZu3Ig1a9Zg9uzZuO6667QbZ319PcaMGYONGzdq6y1cuBDr169HXV0dXnzxRfzgBz/AnDlzNMdo3bp1+Mtf/oKtW7di7969WLx4MebMmYMf/ehHOUmKpuOydB9Lb0NEgbX7GEaNGsVcawZNLKVQIoGWVWBZLAFAcXFRkmE4LwRRNFyOT1+x1BdR9htuXyLRPwxHIIqi4R2zvtB9MaNbxuHoiWG+IYsXL8bs2bMxdepUiKKIq6++Gk899ZT2vt/vx65du9DdHWxiumvXLsybNw9utxs1NTV48MEHMWfOHO19h8OBV155Bb/97W/h9XoxatQozJkzB7/4xS+yum+UoUOHIj8/H52BEFqqiL0dgOyPGkbMJbT5bSpiSTSIWCoqKkJza+INgwXJi8LCQsP0haMEc5b6CENCANlnCrGkOUiEzoaTYTGpqDDaucdR4flY2cMw3/yysjK89NJLUd+vqanpd+LMnz8/ZjXuM888E+vXr9dtjOkiiiJOPPFEbNmyFZAlwJLa4REDDXlPPPFEPYenC1QsCb7UxVKua2LFo7i4GIJUFwzfxEGUvCiuKM/wqPQn6Cz16YMXcJnoe0amfxiOwJrid5J1uLNkTMzkcrIOf5xgDDXHiEDscae8DRrGYy1fCQAGDx4MABC9sVvVREL0daK0tJT5WVZaMcYEm+kKsteQBRw1QdQnDEeFk7nEUuAhjCiwWs2Z28OdJQ4nNvwbwhhU4Fi6UxdLYrcbgiDiuOOO02tYuuFwOFBeXp58GI4QiN4uzZliGZqondCMOEUCFNnQYqmvsySYyFnS3JY+pQPMmght1v3icPSCiyXGOP744wGos9lSghBYetwYPnwYsw5MdXU1RJ8nqabBgr8HIArzITigTy5PhL5p4Ri5NUhksWQ+Z4kmeAvgYslI8BAVR0+4WGKMESNGwGq1wtLdmtL6gr8bguTVRBeLVFVVAUSB4O+Ov3AAwaeG7WgYj2WiFmuMgJFbg+Tn56v/6bufgf9r7xmYYGiKhuGIKUUFYM4wHE9+5uiJ+b4hBsdqtaKmpgaW3taknBeKGAjfsRiCo6SSt0SXNZSzFKFvWj8M3BokorOkqP83g1jqn7NETJsIbVYRyOHoBRdLDHLccceprT1SmF5Pc51GjRql97B0g4olwedJeB3RiM5SImE42bhiiQqiSGE4c4klOquRmNKBAXjIisOJhzm/+QaHCh2xpy3pdcUeNXwXq6VLrtGcJV/izhIVVrmorJ4swVli8WfDGTkh2mq1wmazaW4SAG2fzSCWwsNwAjGfWKKhKu4scTixMdc33yTU1NQAACw9yectiT1tcOblMe3A0BY3QhJiyUjOUrTK1pEwekJ0Xl5eiCik+5OXlxdtFcOguS1aNNx8YonCnSUOK7B6Lprzm29wqFhK2lkiCizedoyqqWH2hAOC7pDgSybBuxv5+fmGEBURizVGwcjOEqA6SJFylswglrTZcAhtd2JGeDI0hxMbc37zDc7gwYPhzMuD2NuW1HqCtxNQZIwcOTIzA9MJl8ul7l9SOUuehJouswAVCiHhqWgYPGyVl5cHQZGCL8jq/426P32J5Cyx/BCSDmbdLw5HL7hYYhBBEDBi+HBYetuTmhEn9qr9yFgXS4IgYFBlJcRESwcoEgSp1xD5SkCSOUsGnz2Wn58fIgqNvj996Vc6AOacYs/hcOLDv/mMMmLECFUkJOO+9BhDLAFARUWFWmgS8cWguhxQXm6M/mmRZolFxeDOktPpBBQ5OGMssD+sFkRNhqCzFEzwNqsDw8NwHE5suFhilBEjRgBAUqE46iwNHz48E0PSlYqKioSXFQO5Tcmsk0usVqtajyeBMBwNYRk1x0cbd2A/6P6YSixpgt68Ysms+8Xh6AUXS4wybNgwAIDY25HwOmJvO0RRNET/NE34JPBESyt9GyVnCQCczjwIshR3ObqMUcUFHTfdD0GRgmLR4EQSEFxUcDgDEy6WGCUVsWTxdqC6utoQNyoaUhMSCsOpYqmsrCyjY9KTvPy8xBK8FT8EQYDD4cj8oDJA0FkK7Kvsh9NpTJcsnH7CyIRhOLo/ZgzDme1YcXILF0uMooklb4JiSfZD8Pdo67GOJnwScZZ8PaHrGIA8pxOCIsddTlAkOJxOw17YNWdJC8PJyMszpksWjlGPSSqYeV/NvG+c7MHFEqPk5+ejpKQk4ZYnVFQNGTIkk8PSjWCydnyxJErGE0tOpxMCiR+GgyLDaVBXCUDQEQsIQ0GRDBtSHIiY0VHicDIBF0sMM2TIEFgSFkud2jpGIClnyYBhOLvdrtUcioWgSIYNwQF9c5YCYThi7P3hcDicSHCxxDBDhgwJTMlOQFAYTCyVlJQE/peIWOpFXl6+oW7CDocjoWMnKLKh9iscbewk4CzJJnSWBoD7YmaHycz7xskeXCwxDJ3VJmhdz6MjertC1mEdl8uVcCK6IPWitLQkswPSmYQFkMGdGDp2QZZUcUgU1VUzEwMg58XMeT1m3jdO9uBiiWGCTWMTyOsJiKWqqqoMjkg/BEFQ3aV4T32EQJR6+zhRxiBRwSAosqHFheYiEVnLWzKds8QxJNRR4s4SRw+4WGIYTfgkNGOsCwUFhYaqBK0KoDj7pkiAIptWLMHgYomOXVAkbUackfenLwPpJjuQ9pXDSQUulhgmGbFk8XlQVTU47nIsUVxcjHgGuSD1assaCZvNFvhfrGNHwpY1HpowUoLOkpHDin3pJyAEwbSigoeqOJzYcLHEMMGK1XEu0LIPkH19wnbGIBEBJEheAEBRUVGmh6MryQggI4slLWdJkbUkbzM7S2YTS1QkcbHE4cSGiyWGcTgcKCoujussiYFmu0ZqBwIkJoCos8TFEptowojIWhFO84klQfvXbGKJYsb9GggC0IzHjVW4WGKcQZWViDv93Ge83mkAUFhYGHcZQfIBMJ5Y0mb6JXAtM0J7mmhoQq9PGM50YonedE147x0IgsLM+2jmfWMNLpYYp6KiIn5eT8BZClbFNgYJOUuyGoZLRFixRDICyMhiKZjgLUMweRiOQICixC/jweFwUodVt4yLJcapqKiIu4wYqHCdyLIsUVBQEHcZmrOUyLIsQQVQ7EbBJGRZI9I3DGdaZ6nP4wqrF/JUGQjT6/m+cfSAiyXGScQtEvxq7zRTiqVAGw2jiiW9l2UNGoZTE7yVkNeMTv8wnHlzlswYzjHrseqLGY8bq3CxxDgJiaVAzpLRwnAJCSDZmM6SKCb+1bJYLBkcSWbRhBFRtARvs4glGnIjfRK8eRiOwxmYcLHEOKWlpXGXEaUeWK1WwwkKl8sVdxlB8ie8LEskI4CSEVasERRLwdIBZhNLQcwnlgZCGI5jLFh1y4x7lR4glJWVxV1G8PegtKyM2ZMsGolUGxcUY4qlhARQ4P5kBmdJUBTTheE0YRT4XhEehuNwBixcLDFOYs5SL8oSWI41EmrNIvtgsVoNlzScjFtk5BtVsESCojV8NnIOVl+CLlLw+MiynJvBcDicnMLFEuPE74lGANlvuN5pQILOkuxHfl5eFkajLwMlZ8lisahij5jPWYqU4G22MJyZ4dXJjQmr7i0XS4yTl6BQMKJYSqSHmKBICf8NWCJ4gWbzi68XgiCogonIgGIuZ0lzkQTzJ3izeoPSAzPvGyd7cLHEOIIgxH4yClwIjNZoFkjsiU9QpMTCdYyR0L4F/jVygjcQEEd9nCUjO2V96ReGEwTThuG4+8LhxMbYV+kBQqybKS16aESxBMS/SAuKBKfTmaXR6EdiAkg9dka/UVkCYsm0OUuCeiwJRCjcpeBwBiRcLBmA2DdT9eJttN5plLhCQZYSCtdxcofVEuosmU0sEcH8zhIPVRkTftyyBxdLBiB2GE79x2i90xKDACCGFEtGd4uSwWq1qOFgk4mloDDqk+Atmytnid9sjc1Aus7kGsOIJbfbjZkzZ6KoqAglJSW45ZZb0NXVFXOduro6XHXVVaisrERRURGuvfZaHDlypN9y7733HiZNmoS8vDyUlpbiyiuvzNBepEbskI7JnSXAkGG4gYSa4K1o+XNmyVmKnOBtTmeJw+HExjBiaebMmdi+fTuWL1+Od999F59++iluu+22qMt7PB5Mnz4dgiCgtrYWa9asgc/nw+WXXx4yo+WNN97Aj3/8Y9x8883YunUr1qxZgxtuuCEbu5QwiQgKozpLieyb0WosAQPrid0alrNk9IR1SnhRSgii6cJw3JngsAar56Qh/PIdO3Zg2bJl+OyzzzBx4kQAwIIFC3DZZZfh8ccfx5AhQ/qts2bNGuzfvx+bN2/WXJdFixahtLQUtbW1mDZtGiRJwj333IPHHnsMt9xyi7buuHHjsrNjCZLIbDijtTpJDHXfjCiWBhIWSyAMFzhe5gvDBRK8TVxnidUblB6Yed/MCKsPmik9Avb29uKxxx7DZZddhokTJ+LMM88M+dGbdevWoaSkRBNKADBt2jSIoogNGzZEXMfr9UIQhJB8F6fTCVEUsXr1agDApk2bUF9fD1EUccYZZ6C6uhqXXnoptm3bFnM8Xq8XHR0dIT+ZJJEEb6OKpUQuZEYscpjMF57Vi0OiWCwWdVamyZwlKpZIYDacGZ0lM8P73nH0JKVHwFtuuQUffvghrrnmGpx99tkZV+5NTU0YNGhQyGtWqxVlZWVoamqKuM4555wDl8uF+++/Hw8//DAIIXjggQcgyzIaGxsBAHv37gUA/Pa3v8WTTz6JmpoaPPHEE7jwwgvxzTffRO3L9sgjj+Chhx7ScQ9jk8jf14i1iBLFvGJJSGJZdhFFMZDgTYK/m4B+YbhAUUpCiOncCqOfgxxOpklJLL377rv4v//7P5x33nlpffgDDzyARx99NOYyO3bsSGnblZWVWLJkCe644w489dRTEEUR119/Pc4880ztYk4vhg8++CCuvvpqAMDChQsxbNgwLFmyBLfffnvEbc+bNw+/+MUvtN87OjowfPjwlMaZCPGcJWdenmGTahOZ6WfEsE7w5hN9/0jgXaPfqNTvEwFgTmepb84SoF43jPp9i4bZxB+Hozcp3YWGDh2qS0Lxvffei5tuuinmMqNHj0ZVVRWam5tDXpckCW63G1VVVVHXnT59Ourq6tDS0gKr1YqSkhJUVVVh9OjRAIDq6moAoTlKDocDo0ePxsGDB6Nu1+FwZHU6ezxB4TKxqwQYXSzpuyyLiKIIoY+zZBYhERRLNAwnaK+bZR85HE5ipHQXeuKJJ3D//ffjueeew8iRI1P+8MrKSlRWVsZd7txzz0VbWxu++OILTJgwAQBQW1sLRVEwadKkuOtXVFRo6zQ3N+OKK64AAEyYMAEOhwO7du3ClClTAAB+vx/79+9Pa7/0Jp6zZNYQHK1ObsQbUzKJwEZPGlYTvBVVMMGYxysSkeoshb7O4XAGCimJpYkTJ6K3txejR49Gfn5+v5wSt9uty+AoY8eOxYwZMzBr1iw899xz8Pv9mD17Nq677jptJlx9fT2mTp2Kf/3rXzj77LMBqCG1sWPHorKyEuvWrcM999yDOXPm4KSTTgKg1ib66U9/it/85jcYPnw4Ro4cicceewwA8IMf/EDXfUiHWGJJAOByubI3GJ1JxP434s13IImlYNjNXDlL4WE4EpgPI0lSroaUMYzubkaChxY5epKSWLr++utRX1+Phx9+GIMHD87KSbl48WLMnj0bU6dOhSiKuPrqq/HUU09p7/v9fuzatQvd3d3aa7t27cK8efPgdrtRU1ODBx98EHPmzAnZ7mOPPQar1Yof//jH6OnpwaRJk1BbW4vS0tKM71OixPv75uXlZWkkucG0YklIYlmGUc9PouWYmeUmFWk2HGD849UXeqzMInAjYZbzkZNbUhJLa9euxbp163DaaafpPZ6olJWV4aWXXor6fk1NTb+no/nz52P+/Pkxt2uz2fD444/j8ccf12WcmYCLJZOKpRSWZRFtNpxpnaX+OUtmw4zOEi8dYExYFbcpXdXGjBmDnp4evcfCSRGziyVWvzyxSOaGavSbryAIIWLJiMcrElq4LWw2nBnDcEYX7AMVLgSzR0piaf78+bj33nuxcuVKHDt2LKsFGjn9MWKj2WQwolMx4MQSgrPhzCKWwp0lYkJnid5suVgyJmb5rhmBlMJwM2bMAABMnTo15HVarM1MFxMjYGRnyaxf9mS+A0a/UYWLWbMc02g5S2a8vnGHgsMKrJ6LKYmljz/+WO9xcNLA7M6SEW++2o0WQoyylOo7pgnrmMxZCh4X85cOMLpgj4RZzsNIsCoo9IDVfUtJLF1wwQV6j4OTBmYXS0Zk4IXhAG06nEnon+BtXmfJjGKJYmbRZEZYPRdTTgZZtWoVfvSjH2Hy5Mmor68HAPz73//WmtRysgcXS+wRvNEmsaxBCb8ZmeXm1E8sgYslDifTsOospSSW3njjDVxyySXIy8vDpk2b4PV6AQDt7e14+OGHdR0gJz52uz3XQ8gorH55YpHMDdUsYTjBZLPh+id489lwRsSI14+BDKvnYkpi6Q9/+AOee+45/O1vfwup3n3eeedh06ZNug2OkxhGFktmvZD5/f6ElzX6zVcTRyY7lPS4OA6sRf72t2FtPxTyuplg9QalB2YR7wMFVs/FlMTSrl27cP755/d7vbi4GG1tbemOiZMkRhZLicDqlycWydxQzXjzNQPUWRJ7O2DpPgZB6g153UyY9aGFYzxYvd6nJJaqqqqwZ8+efq+vXr0ao0ePTntQnOTgYok9+jVhjYjJZsOZjGBRSvqK+Y6XYOIZfhQuBI0Fq9f7lMTSrFmzcM8992DDhg0QBAENDQ1YvHgx5s6dizvuuEPvMXLiEN7I2Gyw+uWJRcJhOEE0z83XZNGOaILXTMJiIBSl5GE4Y8HquZhS6YAHHngAiqJg6tSp6O7uxvnnnw+Hw4G5c+firrvu0nuMnDiYXSwZ8eaUsAAygVjSenAFRIVZnuSjHRcjno/xMMsx4xgfU4klQRDw4IMP4pe//CX27NmDrq4ujBs3DgUFBXqPj5MAVmtKh5EJErlIG/HmlKgAIqLxxZJZkWW5T9kAgIfhOJzMYyqxRLHb7Rg3bpxeY+GkiJHFUixo9WsjXsgTD8NZkpo5xyJBwWsuZ6mfWApEc8wkliis3qD0wCzn40CB1et9SnfZq666KmIcWBAEOJ1OHH/88bjhhhtw0kknpT1ATnzMKpYoRhQTwTHHzpcggmjI/euLdjMSTC6W+r5uMswslsyImfOwWD0XU0rwLi4uRm1tLTZt2gRBECAIAjZv3oza2lpIkoRXX30Vp512GtasWaP3eDkRMLJYinljNfCTvN/vB0RL/AXNJJai/G5UJEkKNtEFYMYw3EBI8OYYC1avHyndZauqqnDDDTfg6aef1jqOK4qCe+65B4WFhXjllVfw05/+FPfffz9vf5IFjCyWEsGINye/3w8I8cUSESzw+XxZGFHmMGsYTpIkzS3rixmdJTPuk1nOw4EGq+diSs7SP/7xD/z85z/XhBIAiKKIu+66Cy+88AIEQcDs2bOxbds23QbKiY7FkoCDwSiJXNCM6Lz4fD6QRJwl0QK/AcVgXwgharhK6PO7CejvLKmwejFPB+4scViB1e9XSmJJkiTs3Lmz3+s7d+7UdtTpdJo6rsoSRhZLsVHPHyOKJb/fH/FG2w9BhN9nvP3rS/BGK4T9bmwkKTzB23xhOIpZjhnH+LB6LqYUv/nxj3+MW265Bf/93/+Ns846CwDw2Wef4eGHH8aNN94IAPjkk08wfvx4/UbKiUpfh89oJOJCGDFM5dPCcLG/+ES0wO/3gRBi2IcLRVFUISGYTCzJkcNwXCwZA6N+nwY6rJ6LKYmlP//5zxg8eDD+9Kc/4ciRIwCAwYMHY86cObj//vsBANOnT8eMGTP0GyknKkYWS4lgSGfJ5wMRRQjxHGXBAkIIZFk2bO6ZoiiAosDadhiAecJwsiRHTPBmNUyQDmbcJwoXTcaC1XMxpauzxWLBgw8+iAcffBAdHR0AgKKiopBlRowYkf7oOAlhZLGUyI3V6/VmYST64vP5ACEv7nI0r8nn8xlbLIFAkNXjxOrFLllUZ6l/30UzOUta9XWTCFyO8WHVWUr7LltUVNRPKHGyi6lzlgTRkGIp8dIBQbFkVNQbbfDpndWLXbLIkmT6Okv0WJnlmEWCC0Fjwer3K+VH2ddffx2vvfYaDh482O9Cv2nTprQHxkkcI9vMcS9kFqvhhAQhRE3wzrPE7y0rGl8sybKMvnLJLDdeSZIAqwgQ9eJNTJjgTY8VqzcoPTDy9XEgwuq5mJKz9NRTT+Hmm2/G4MGDsXnzZpx99tkoLy/H3r17cemll+o9Rk4czByGI4IVvb29WRqNPmjCR4j/LEJMIpb6YhaxJCuKqUsHEEJ4UUoOc7D6/UrpLvvss8/ihRdewIIFC2C323Hfffdh+fLluPvuu9He3q73GDlxMOqTUyL2OBEt6DGoWCKJiFgThOHCb7SsXuySQVEUEEWJGIYzi7PU9zhxscRhhb7nJUsh1JTE0sGDBzF58mQAQF5eHjo7OwGoJQVefvll/UbHSQijiqVEcpGIaEVvT08WRqMfA9JZ6nMOmkEsaYIo5LtlrjBcX4FkhmMWDZZuuJz4sHpepiSWqqqq4Ha7Aaiz3tavXw8A2LdvHz8xc4BRxVJC4TXRZrgwHBWBiVbwBowtlsLFA0sXuFQJiiXzhuEGirNk1OvjQIXV8zIlsXTRRRfhnXfeAQDcfPPNmDNnDi6++GL88Ic/xFVXXaXrADnmpScBx4hYrPD7/YZ6mteEzwARS+HiwQxigu4DMXEFb1af4DkDm77nIkvnZUqz4V544QXti/azn/0M5eXlWLt2La644grcfvvtug6QY14SEkuieor29vaioKAg00PSBS28mIBYIoFQnRHLI1BU8WCuMJy2DybOWeorllh6gtcbM0c7zLhvrIr4lMTS4cOHMXz4cO336667Dtdddx0IITh06BAvSMlJiO7u7vgLWWzaskYTS1ToxSQgqIwtluSw340vJrSq8QNELJnxpksxcxjOjPtmqjDcqFGjcPTo0X6vu91ujBo1Ku1BcQYGiYglEhBLibhQrJBMGI4KKkOLJTlUPJhBTETOWTJXuxNWn+D1glcnNyZ9z0WWriUpiaVoTT+7urrgdDrTHhRnYJCQWBKDzpJR0JylBGbDmcFZUitdB68HLF3gUiVizlIAM+wfMHDCcBxjYYqcpV/84hcAVOvv17/+NfLz87X3ZFnGhg0bcPrpp+s6QE58jPrklFgYTu3N5fF4Mjwa/dBm71kSKR1gbGeJEKKKByHooplBTER0loSw9wxOyE3JhGLJqNfFgQ6rYbikxNLmzZsBqCfhV199Bbs92GTSbrfjtNNOw9y5c/UdIScuRr0oJCKAaBjOSGJJK0opJDIbzthiSZZl9fzr4yxp+T4GJlYYzixiKcRZYugJXi/MmM8zEDCFs/Txxx8DUMsF/PWvf+UNdBnBqGIpsZwlAztLCSR401pMRqslRYkkHMwklvqF4QSBqQt4OgyUnCWW3Am9Meq1Pxas5iylNBtu4cKFeo+DkwZG/cJ0dXXFXYZYjSeWtJylBMJwRneWgrPGzOUsRS0dIIhMXcDTgdVwh17Q/TPjvlHM6J6xel6mJJY8Hg/mz5+Pjz76CM3Nzf12aO/evboMjpMYLJ1QyZCIWKI5SwktywjJOUvGFkuRimmaQSxFLR0gWEyxf0CYs2TQa0gsqKg1i7gdKLDqeKYklm699VZ88skn+PGPf4zq6mpTqlsjYWpnKSCWaP9BI0DFUkJ1lizBoptGJCgcgtcAI1cjp0Rrd0JMFIYLeYKXzSeW6P6ZWSwZ9dofC1OF4d5//3289957OO+88/QeT1TcbjfuuusuLF26FKIo4uqrr8Zf//rXmIUK6+rqMHfuXKxevRperxczZszAggULMHjwYADAypUr8e1vfzviuhs3bsRZZ52VkX3RG6M6S4kIIGJ1ADC/s2SkOlJ9oWKJCEG5ZAbnRSsdIPZ3lli6gKdDaOkAcwjAvtBjaBZxGwkzGhWshuFSqrNUWlqKsrIyvccSk5kzZ2L79u1Yvnw53n33XXz66ae47bbboi7v8Xgwffp0CIKA2tparFmzBj6fD5dffrl2ACZPnozGxsaQn1tvvRWjRo3CxIkTs7VraWPUi0FnZyfiPReZ3lkyTRjOXM5StDAcEQRTiEHA/HWWqKg16vVxoGKK2XCU3//+9/h//+//YdGiRSG1ljLFjh07sGzZMnz22WeaiFmwYAEuu+wyPP744xgyZEi/ddasWYP9+/dj8+bN2qy9RYsWobS0FLW1tZg2bRrsdjuqqqq0dfx+P95++23cddddhlLsRr3QqQJIAGJJJtEKCKIxxVIiCd6CCIgWw4bhzCqWgmG4sPIPpnWWjHkNiQUVtWYRtwMFU4mlJ554AnV1dRg8eDBqampgs9lC3t+0aZMug6OsW7cOJSUlIW7PtGnTIIoiNmzYgKuuuqrfOl6vF4IgwOFwaK85nU6IoojVq1dj2rRp/dZ55513cOzYMdx8880xx+P1ekOcgI6OjlR2SzeMeqHr6OiIq5UgCCBWR87/xsmQTBgOUB0oo4bhtO+BEOE1AxM9Z0k0zc2X1XCHXvAEb2NiqpylK6+8UudhxKapqQmDBg0Kec1qtaKsrAxNTU0R1znnnHPgcrlw//334+GHHwYhBA888ABkWUZjY2PEdf7xj3/gkksuwbBhw2KO55FHHsFDDz2U2s5kACNe6Hw+nyoqBBGx1RKgWIwlllThI/R3JaJgCrFkMmcpmIsVqXSAOcRSeCPdaG2sjApt8MzSDZcTH1ZFfEpi6Te/+Y0uH/7AAw/g0UcfjbnMjh07Utp2ZWUllixZgjvuuANPPfUURFHE9ddfjzPPPBNieNImgMOHD+ODDz7Aa6+9Fnfb8+bN01q/AKpDMnz48JTGqQcsWZWJEgyrxb84E6sDnZ0thrmY9/T0ABZbSO2hWBDRhh6DhuEiiSUzO0sQRfhNcvMNvxHJsgyrNaVbApNQUWuW4zVQMJVYonzxxReamBk/fjzOOOOMpNa/9957cdNNN8VcZvTo0aiqqkJzc3PI65Ikwe12h+QchTN9+nTU1dWhpaUFVqsVJSUlqKqqwujRo/stu3DhQpSXl+OKK66IO26HwxES3ss1RhRL7e3t6n/iheGgiiVZluHxeGLOfmSFnp6exJK7KRYrerqNM9uvL1rIUYjwmoGJnuBtzjAc/d1MYsnvD4ThTHK8BgqmCsM1Nzfjuuuuw8qVK1FSUgIAaGtrw7e//W288sorqKysTGg7lZWVCS177rnnoq2tDV988QUmTJgAAKitrYWiKJg0aVLc9SsqKrR1mpub+wkiQggWLlyIG2+8sV/+lRFg6YRKFE0sJeIs2ZzaOkYQS93d3Ykldwcgog29nl4oihLR9WQZsztLkUoHyJJkGJczFuFP7Sw9xeuBJNOcJeM9TA5k+h4vls7JlK7Md911Fzo7O7F9+3a43W643W5s27YNHR0duPvuu/UeI8aOHYsZM2Zg1qxZ2LhxI9asWYPZs2fjuuuu02bC1dfXY8yYMdi4caO23sKFC7F+/XrU1dXhxRdfxA9+8APMmTMHJ510Usj2a2trsW/fPtx66626jz0bmF4sWVWx1NbWlrkB6YjqLCUhug1cmDLoLAn9XzMwQWcpLO8sIJ6M6OaGE74PLN2Y9CCY4M2dJSMhy1Kf/7PzPUvJWVq2bBlWrFiBsWPHaq+NGzcOzzzzDKZPn67b4PqyePFizJ49G1OnTtWKUj711FPa+36/H7t27Qppzrpr1y7MmzcPbrcbNTU1ePDBBzFnzpx+2/7HP/6ByZMnY8yYMRkZe6Zh6YRKFE34JPB0TsVSUGCxCyEEPb29IK7Em0xTYdXT05OVUhx6EkxMV48jgWDYZPW+RA/DqeLJ5/MZPmRlZmdJlmWQwP4oigJZlmGxJDbhwkiYsYJ33/OQJSMgpW+7oigRw1U2my1jX7iysjK89NJLUd+vqanpd+LMnz8f8+fPj7vtWNs1AiydUImSjEtkJGept7dXvUgn4SwRi7psd3c3ysvLMzW0jKDVlKKiVxBM4SwFw3DhdZbEkPeNTKQEb7MQfnwkSTKlWDJ6KDgSrCZ4pxSGu+iii3DPPfegoaFBe62+vh5z5szB1KlTdRscJzGMeOGmwock4iwFcpZaW1szOSRdoM4mFUAJ0UcsGY1wZwkmcZa08gcRGukC5ih0GCnB2yyEl68ww/EaKLBalDIlsfT000+jo6MDNTU1OO6443Dcccdh1KhR6OjowIIFC/QeIycORhRLQeGTSBguL2wddqFCIRmxRFu6GFosac6SMfcjHO07Fe4sBXKWzFBLysxhuPBrIhdLxsFUs+GGDx+OTZs2YcWKFdi5cycANQk7UlVsTuZh6YRKlNbW1sBTeyLOkiqWjBCG83g86n8CAigRaM6Stq6BCAqjoLPU22vMmX19CRalDBVL9HcjfufCMXOCd7g4MsPx6osZw28UU4ThamtrMW7cOHR0dEAQBFx88cW46667cNddd+Gss87C+PHjsWrVqkyNlRMFI14I3G63losUDxqGc7vdmRySLqQShiNmCMP1yVkCjD8jLmoYzkTOEr0p0VA4SyGPdImUs8QxBqYIw/3lL3/BrFmztMa0fSkuLsbtt9+OJ598UrfBcRLDiBaz2+2GEnCM4iKIIFYnjh07ltlB6QB1h5IrHaC6UF1dxitM2d3dHRaqUm+8RnTJ+qKJITGys2QmsQQTuWWU8GuiEa+RsTDjLDhA3a++bpJhxdLWrVsxY8aMqO9Pnz4dX3zxRdqD4iSH0S7cvb29auHGRMUSAMWWZwhnSRNL1iTCcAZ2ljwej5ZzBQRdCiPuS1+iheGoeDLDzVdzlgLV5lkKeaQLz1kyJizP0ExKLB05ciRmhWur1YqjR4+mPShOchjtiZA6RIot8ZpCxJaPzs5O5oWh5g4lk7NkYGfJ4/GEuWjqJcWI+9KXaM4STOwssXRjShez5yyZFa1kh8Be8dekxNLQoUOxbdu2qO9/+eWXqK6uTntQnOQw2oWbiqVknCW6LOvukuYsJSOWAi6UEUNXXV2hzhJM4ixFy1midZeM9p2LhHYjEs0nlnjOkjFh+ZxMSixddtll+PWvfx0xebOnpwe/+c1v8N3vfle3wXESw2gWsyaW7Ik7S9SFYj1viToqyYglo+YsybKMnp5uEGsfZykgloy2L+F4vV5AtPavMB8IWZlJLNH+d2YSFMEwqnkS8iNhttyl4KQD9sRSUqUDfvWrX+HNN9/EiSeeiNmzZ2s91nbu3IlnnnkGsizjwQcfzMhAOdEx2oWgpaUFgBpaSxQqrOi6rKKJpaRylhwh6xqFoIvmgCDRc1AVF52dnTkalT74fL7+1bthUmdJMHHOkmgFZB9TN109MVsJAe0cZNBZSkosDR48GGvXrsUdd9yBefPmaapWEARccskleOaZZzB48OCMDJQTHaNduKngUeyuhNehwop1sURFQlLOkigCotVwYinivprEWfL5fP2TuwEtv8fr9WZ5RPoTdJbMNxsu2K7GBkH2mWrfzAzLYbiki1KOHDkS//d//4fW1lbs2bMHhBCccMIJKC0tzcT4OAlgVLGUjLOkGMRZ0hyVZMQSAMXqQEdHRwZGlDk0QWTtOxtODXsY3Vnq9Xr7J3cjOHPMTGKJhhbNJCg0sWSxAn7jpSoMVEwThutLaWkpzjrrLD3HwkkRo4mlo0ePqrWTbIkVpQQAEnChWJ9t2dnZCWJ19M91iQOx2A0nMKi4I1ZHn1eFkPeMitfr1YRRCKJ5nKXwZsEs3ZjSJSQMB3Ptm5lh2Vkybj8CjobRxFLz0aNqQcrw6sgxILY8QBCYF0sdnZ1aDlIyEKsDnV1dhsob0cRS3/01ibPk7e3VbrQhmMhZMrOg0BK8TVQXayDAsrPExZIJMJJYUhQFLUePak5RwggiFFs+mpubMzMwnejs6EgquZtCLA4QRTFU+YCIzpIgABDQ3t6em0HpACEkRoK3KiyM3s4F6OssqftkJkFh5hCjmeHOEiejGOkpt7W1FZIkJZXcTVHsLtWVYtR98Xq96O3tDQtLJQZdx0jhKyqIwnv8EavD0GLJ6/Wqk1citKwxo1iCxXyCIrw6OUs3XU50uFjipEW8WhpGEkvUGVLsBUmvS+wFkCUJra2teg9LF4JOS+K5WBQjiqW2tjYA6Jd7plidhhZLVAhFzFmymCcMFwxVqaLQTGIpGGJk76bLiQ4Pw3HSwkxi6ciRIwCQfBgOwVIDTU1Nuo5JLyInPCcGFVhGEhnxnCVWHcB4aK6Rpb9YogKqp6cnm0PKCOE5SzwMx8k13FniZAwCY4klKnQURwrOUmAdVvOWNKclDWfJSGIpuL+h4pDYnFAUxbC1lqgQIhHCcDCRWNKcJYv5nKXwGlJGFe4DjfDjxsUSJyliO0uCofIngs5S8mKJhu5YdZaiOS2JQENZRhJLra2t6r6G90+z5mnvGxFNLEVwliBaAEE0hVgK1iJSxZIpnSUGwzmc6GiCXWAv14yLJQNgpjCcHs4Sq2IpPWfJGbINI9Da2gYlQq0sKvyMLpYiJXgDqrgwg1jSxJFoPrFEnSQWc1840eHOEict4oklIzlLjY2NatgmyQrXAPvOUrSE50QwmliSJAkdHe2ai9QXYjO2s9Td3Q0g6LiEQ0SboUo8RCM8DGcmsRTMfRFDfzcZZm2ky3OWOPojCGprBgNACEFjY2NKM+EAABYbiNWJxsZGfQemE1QcKLb+AiIeVGC43W5dx5Qp2traQAjRxt0X+tqxY8eyPSxdiCuWLDZtGSND67OZWiwJ5s5ZMlsjXS6WOGkR7+lBliRDJGe2tbXB6/VCcRSmvA3FUaAKLgYvflQspRKGg8UGiFbDuDFUCEXq76cEXjOqWNJcI5OLJb/fH8jBUm9MRipuG49gGE4M+Z3DNuGlA1i6r3GxZAASsVqNkEPR0NAAAGmKpUL4/X4mb8RtbW1qsnMKIUYAUGxOuA0mlhR7JGdJFUtGccnCoWIp4mw4ALDYIEmSoXIFI+Hz+dSbkmhescTDcMYimOAtAoLIxRInOeLNhgOMkeRNxRJJSywVhWyLJY4dO6a6Kila48SWj1a32xAXwJaWFgAAsfWvl0XDcKz38YuGJpai1MsiATFs9Lwlr9cLiBYtmdaUYsmkzhIVf6YNwwXEEksil4slk2CEJG+aa5SuswSwJ5YIITh2zB0xhydRFFseJEkyRBNaTSzZ+4fhIFpArE5tGaOhiaWoYThziCWt/90AEEtGeABJBpZEhJ5o5SwEERAE7ixxkiPmFz3wZGGEMFx9fT0AQHEWpbwNEliXbosVPB4PfD5vSsndFCMlRlMhpETIWQLUautHjxpTLGnFNKOEU6lYMmrRTYrX6wURrCCCefrdUeg1kwScd7M5S2ZKxu9L3zAc4c4SJ1kSeSoywoWuvr5e/RKk0OqEQsNwrImlWAnPiUIMlBhNq6hHO5bEno+enm5DCgrq7BFLlLY1gfCcERzAWPT2etWK5KJ5+t1RzO4smVUshYfhuLPESQqzJHgfPnwYsqOwX8XnZCC2PEC0MieWYoalEsRIs8iam5sDx8IS8X1aHoLV1jSx6OzsDMxOjHyeUmfJyGKJEAKvt1etUi6qNyYziSXtmilwZ8lIhDtLXCxxdMQYYTiPx6PW5nGkHoIDAAgCZEchDh8+zNTToi7OUkBosZ7rQwhBc3MzlAjJ3doyAcfJiEnenZ2dUGLMaCQmcJYkSVIFRGCKNkSrIdzpRAleG4Sw382BmfLL+hKas8TFEidJYucsqf+wXvfl0KFDANLLV6IozmJ0d3czVZOIigIlDWeJzixjXWC0t7ejt7c3Zssa1qutx6K9vT3qTDggWEero6MjW0PSHSqMaBI7MZlYCobhzC2WTLZbfZwlgTtLnORJpHQA6xe6w4cPA1CFTrpQwUW3yQJU4KSVj2UQNybYDDn6vlIhRZc1CpIkwePxxCwsSoWUkZoehxPsf6fmKxHRih7GryGpYW6xBJhrv0IqeAsiJIkneHOSIJEv+kBzlvpukwU0ZylGaCouFhtgsTEvloLNkKOXgCABZ4nV1jTR0JK7E3CWzCCWCBVLFivz15BkCM9ZMhtULEkMzRbTAy0XKxCG8/vZCTdysWQAYoklYpCcpYMHDwLQy1liTyw1NzdrYidlBAGyzcV8UnSwXlb0MBxN/jZaGE5rhjxAnKVgGM6G3p4e0zkwxKRiiSbjy5LE1PT6dOmfs8TOvnGxZAASyVliXSwdOnRIbYSbRgI0hYolKsBY4MiRI5DtrrSfZIndpfXQYxUqlmJWYhcEyPZC1DNWPDQemliKVS9LtAAWO1M5c8miuUhU3FtskGXZNInD4ddMs4nAvmkXLF8rkiXoLKmV5SWJnVl/XCwZBCIIUKx5mpNEEPg98JTLslhSFAUHDx2C7CjSxxa3OkBsecyIpd7eXnR0dGihp3RQHOznLWk9/uyxK7ETRyE62tsNVek60WbIitWpCSsjQsUS7X9H/2X5OpIa5naWwv9vdLSE7kA5C0VRmHHOuFhiHEVRQAiBXFAFzxnXQ8kvU1/PL4PnjOvRM+a7ANjOWWpubobP64XiLNFtm7KzGA0NDUzUG6FJzEoayd0UYoBZZPX19apDaLHGXE5xstmaJhYJiyWbE62trYat36OJJRqGC/xrJGEbC7M7S31FLeuTe5JBm+UniFpZC1ZmxHGxxDjhU3zDoa+zLJYOHDgAAFDySnTbpuIshqIoTMyIo8ImnQbBFJo0zapYkiQJTU1NkBNI1KfV1lk4RonidrsBxC8BQWz5kGXZsLWW+rV0MZlYCsdsYqmvQDKTGxgahlPlCSuhYcOIJbfbjZkzZ6KoqAglJSW45ZZb4rZSqKurw1VXXYXKykoUFRXh2muv7TeV+ZtvvsH3vvc9VFRUoKioCFOmTMHHH3+cyV1JCk0EiVEShwNTLFkWS1pyd176yd0U6lJRIZZLtNlhOoThWHeWmpqaoCiKJoRioTDaxy8WweKisXv8GamPXyS0ZsFWe+BfR8jrHHZRq68HQ29mcpY0sSRaNGeJhegBYCCxNHPmTGzfvh3Lly/Hu+++i08//RS33XZb1OU9Hg+mT58OQRBQW1uLNWvWwOfz4fLLLw+xzr/73e9CkiTU1tbiiy++wGmnnYbvfve7zNys4nVAp++x/HSxf/9+ANA1DEddKqbEUozZYYlCt8HqlHs6A5Ek4SyxNGsxHomLJdV5ok6U0aAPmrR1C72+GLGXXyTMXDqgt7c3xClj+dqfLCFhuEArJVacpdhJB4ywY8cOLFu2DJ999hkmTpwIAFiwYAEuu+wyPP744xgyZEi/ddasWYP9+/dj8+bNKCpSL9qLFi1CaWkpamtrMW3aNLS0tGD37t34xz/+gVNPPRUAMH/+fDz77LPYtm0bqqqqsreTUQif4hsJItqYdpYOHDigJuvpUGOJQsUSFWK5hDoneoThiC0fEC3Mi6VESkAQRwEgiIYSSy0tLarLIsbJxwqE6VhOxI9FsFkwFUvGb+EyUAi/1rN87U+WoLNkBeHOUvKsW7cOJSUlmlACgGnTpkEURWzYsCHiOl6vF4IgwOEIFpdzOp0QRRGrV68GAJSXl+Okk07Cv/71L3g8HkiShOeffx6DBg3ChAkToo7H6/Wio6Mj5CdT9JviGwFiscHD6BeGEIJ9+/ZBdhan1UC333Zt+SAWOxNiqbGxUS2LECcpOCEEAbK9gNmk6KTqZQkiZGcRDh48ZJickaNHW7SGxrGgzpLhxVIg/Eb/NXILl8iYz1nSHqAD11MzOktg0FkyhFhqamrCoEGDQl6zWq0oKyuLGi4755xz4HK5cP/996O7uxsejwdz586FLMvaU7sgCFixYgU2b96MwsJCOJ1OPPnkk1i2bBlKS0ujjueRRx5BcXGx9jN8+HD9djaM8FkrEbHY0M1orkFLSws8Ho+uITgAgCBAcZbg4KFDOZ0tQQhBQ0MDZHuBbpY/cRSira2NySfGgwcPqn/7BF00xVmMrq5OQ0yz7+3tRVdXZ0Ita4zcKBjo4yBpzpI99HWDY+bZcNo9ISDYWbxOpIrX6w3kKwkgAbHESmmEnIqlBx54AIIgxPzZuXNnStuurKzEkiVLsHTpUhQUFKC4uBhtbW0488wzIQay7Akh+NnPfoZBgwZh1apV2LhxI6688kpcfvnlMcMg8+bNQ3t7u/aTyTBDeD2USBCLDZIkMaPA+6LlK+VHF5+pIueVQJaknCYQtwfqCOkaYgzk+rDoLu0/cECtlxW4kMWDpUT8eNDK6YmUgDBKH79oaM2CaaNZm/GbA/clPGfJTGKJ5rHSULCZkvK9Xq/WgoeGwlkRSznNWbr33ntx0003xVxm9OjRqKqq6tcCQpIkuN3umHlF06dPR11dHVpaWmC1WlFSUoKqqiqMHj0aAFBbW4t3330Xra2tWl7Ts88+i+XLl2PRokV44IEHIm7X4XCEhPcySTAMZ4+6DBHt2rJ2e/TlcsG+ffsA6Fs2gKLklWqfMXLkSN23nwhUqCUyOyxRaH2i+vp6HH/88bptN13a2trQ0d4OpTTxv3XfRPzTTz89MwPTCXqNSai4qMUGYnUYrlEwpb29A0qfsLEZ+t3FwkxiSXuADgh20zlLgVwlmrPEigmQU7FUWVmJysrKuMude+65aGtrwxdffKHlEtXW1kJRFEyaNCnu+hUVFdo6zc3NuOKKKwAETzLqNFFEUWSm2Fyis+EAdX9KSkqyMayEoWJJztPfWaJu1b59+3DhhRfqvv1EoO6PHsndFFbrE2nCN4mQKhVLdF2WCZaASKy4qGJ3GVIsEULQ3tEO4iwLvmixA4JgGrEUFEfmy1nS7gkBUW8+ZyngWgf+ZaU0giFylsaOHYsZM2Zg1qxZ2LhxI9asWYPZs2fjuuuu02bC1dfXY8yYMdi4caO23sKFC7F+/XrU1dXhxRdfxA9+8APMmTMHJ510EgBVhJWWluInP/kJtm7dim+++Qa//OUvsW/fPnznO9/Jyb6Gk2jOEsDml2bv3r2AaNFVTFD6Oku5ggoaPRoEU1itTxR0CRMXvoqzGBAEQ4klkmAJCMVegO7ubsPl+XR1dUGWJBBrn/IIggBidRq6311fzByGo+UdqKg3S7kHQE1Wp2E4es9jJQxnCLEEAIsXL8aYMWMwdepUXHbZZZgyZQpeeOEF7X2/349du3aFWJK7du3ClVdeibFjx+J3v/sdHnzwQTz++OPa+xUVFVi2bBm6urpw0UUXYeLEiVi9ejXefvttnHbaaVndv2hoAihBZ4klZFnGvv37ITtLdJ0JRyG2PBCrE3V79+q+7URJZip9ohB7IZNT7jWxlEz+mWiF7CjCvn37mL9h0TzFRJPX6QMAi7llsaC1ocJrSSlWJ9xuk4klmFgsBUS9mcRSb29vsABzQDSxMtvPEHWWAKCsrAwvvfRS1Pdramr6fSHmz5+P+fPnx9zuxIkT8cEHH+gyxkwQrLMUI2fJEsxZYonGxka1J1x55mYLynmlaKivR29vL5xOHabuJ8mhQ4fUmiBxihgmhShCdhTi4EEGxZIgQnEkJwyVvFJ0tO6H2+1GeXl5hkaXPo2NjaoLmkDpACAoqhobGzW32gjQmYk0qZtCbHno6lD7LdpsMZxsA0FM7CwRWz4giKYRS7Isw+/3g+QFnCXGxJJhnKWBSiI5S2DUWdobcHzkDMyEoyj5pSCE5KTekqIoOHjwYKCGlL65EcRZhPb2NmZCPIqioK5ur7qvYnKXDdr8ua6uLhND0436+uRKQCgGdZaiVSmnv5shFKflnArBmc9moW/1dWKxm0YsacYAnQ0XuK9xscRJiLi94cBuqwIqlpS8sjhLpo4c2PbeHITiWlpa4PV6dQ3BUWgSNS0CmWuamprQ09OtCZ9koMdoz549eg9LN7q6utDe3gaSxKxGmlvGWiJ+PKhYUmyhiey0GGdLS0vWx6Q34TlLrEzY0QNNLFkdIFY7Mw9U6RJegJmWy+FiiZMQHo9Hm6kSDRqGY+WkomhiKYUbbKLk0rVIqpp1ktBtspK3RP++cgrH0gjOklYCIol6WWpumcBcIn48qBgi9tBwI/3dqM2B+yLLsvqfgLNkJrHUt6AosTjQ2cnWQ3KqhLf2ItxZ4iRDd3c3lDh9qqgCZ2023J49e9QkbD3zecJQp6YLOXGWNLGUlzmxxEoxR+oKpeISErsLxGLH7t279R6WbgRnNSZRL0sUodgLcJARQZsotJBmeG6W0Vu49IWKI2JSsUSs9sAMRjt8Pi8zM8bSQUs5oVEUxmZ5c7HEOF1dXTGTuwFoBStZOakAVeQ1NDRkpL5SCKIVsrMIdXV1Wc9L0KqTOzNTnRxgRyx98803AADZVZH8yoIAOb8chw4dYuYpMZygS1iS1HqKswStbjdzIfBYNDc3A4LYfzZcoG5PeAFgIxLMWbKE/m4COjo6tMbH9F8jnX/R0O5fVtqCh4slThJ4PN2AJY6zxNhJBfSdZp65EBxFyStDR0dH1nMtDhw4EOhRp1/1bg2rA8SWx5RYUuwFgDW1yvVKfjkIITlxABMh1ZAqa+HSRDjS3Ky2yggL7ROHmsNkxEKb4WhhuEBhQ+13E9DR2RkUSyZqgByczBQwB0QrIFqYEYJcLDGM2u/NG9dZYrHOEg3byBlM7qbQuj/ZzonZt3+/OiMqwT5pySI7S9DQ0JBzi93tduPYsWMp5StRZJdaMmDXrl16DUtXDhw4oM4uSjJkTEOwrIjaeEiShGMtLZqL1BdizQME0RTOEhVHtGWGWcSS3+9Hb0+PJpLMJJb6zvKjsDTbj4slhglW744XhmPPWaLCJRvOkpyDBOLW1lZ0tLdnNMyo5KllEXI9I44KHCWVEFwAOV9dl4bzWEKSJBw8eFCrNp4M9PjnonRFKhw9ehSKokSuqC8IUBwFaIjRRNwoSJKk/ifgymu/GxwqiswolmjiOunjXisWOzoYme3HxRLDJFK9G4A648NiY08sCWJGZoqFo+RgarqWr5RRsVQCIPd91ahYSilfKQBxFoFYbEw6S/X19ZAkKSXhS3OcjCKWaE2oSM6S+nohWt1uZvpxpYokSep1URABQYDf78/1kHSB9u4LiiXzNEDWhKClT6jf6kBnRwcTdbK4WGKYhJ0lqDMIWAnDqQUM69Q2JxkKUfWF2F0gVkdWxVIqTWWThYXed0AfZyk/dbFEk7z37z/AXJJ3Kj3vNKx2KHYX6urYzMUKJ15LF6MW2gzH7/cHWywJFtOIJTM7S+H7Rv8vSRIT1wwulhgmoerdARSLDR5GxFJDQwN6e3uzEoIDoN6I88pw+HB91p6IU+qTliQyA84SIQQ7duyA4ijs1x4jWWRXJQhRmCshEKwHltqxVPLKcPRosyGKA8arJ8VqE+dk8fl8Wvd6IoqmEUtBZ8kZ8q8ZnCWtDY81eJ1haf+4WGIYzVmKUb1bw2KDh5FEOC25O1tiCWpuFCFK1oTF3r17U+qTlhRWR85di+bmZrS1tUUMweVvewuuzS/DtflliN1qc1ax2w3X5peRv+2tfssrrkoAwI4dOzI76CRJp+Cmul5uJhikQrzGz0atSh6O1+vVygZAsOR8koRemFkstbe3ayklFCWwf1RI5RIulhgmvO5ELIjFBq/Xy8Ssj2wmd1PojS4broU6BX5fSn3SkkXJK82pa0GFjRwQOn0R/L0QpR6IUg8EqDkFAoj6u7+/w0cFF2tiac+ePWr1amtqzhnNmTOKWCJWR9QSEEYshRAJ1VkK9IUTTSiWbFQsqceRBTGRLq2trVBseSGTLFjqVxi7gA8npwQbC8Z3logYbHlSUBA5eTNbBJ2l7HWYz2ZLDa1PWll1xj9LziuFtf0w9u3bh1NPPTXjnxfO119/DSC9mXAUYneB2PK0bbJAR0cHjhw5Arl4eMjr+dve0gSfIKn/UteM2JzoPvkqbVl6nrMWXgxHkiQcrq+H4oz+EKO2cBFzPgMzXXp7vVpDViJYTSOWgqGqgNgVrYBoNYWzdMztBrGGV5VXxZLb7c7FkELgzhLDJJOzBIZqLe3esweK3ZVyAcNUUJwlgCBmRSylG7ZJBioCc1XM8euvvwYEMa2ZcBqCAMlViebmZmaatQYrk4cK+2RcM+IsAiw2Jssi9KW+vh6yJGm5cBERRcjOIuzbv5+JGUip0tPTE2w+brEykSCsB0GxFKgHJghQrA7DO0vd3d1q/ajwfoU2dvoVcrHEMOFdmGNBBVWuC3i1t7ej5ejRrIbgAACiBbKzBHv27Ml4a4N0E4KTIZchHr/fj13ffKOKwjj9CRNFKRgEAMy4S9QNUtJxQQUBUl4Z9u/fz7SDkWh7HsVZCk9XFxM3qFRQFCVQzDfgLIlqioIZWp5ES4JuNbhYog9PSr9+hXkh7+cSLpYYJhlniZUOzZrrkoXK3eEo+WXo7e3N+LTndJrKJotaKDE7jlk4e/bsgd/ni5ivlCoyY2JJqyGVZshYcZVrJTNYRRP5sZylPu+z2pomHuHpC/TaaPTaUYAqlojVEZIrSWx56O3pMfT+UTEU3txZsasteFho7szFEsMkU2eJNtPNdRgumNydvXwlCg2LZbreUl1dHYjVmXRrjJQQRch5Jdi7d2/Wn4y3b98OIChw9EDOrwAEEdu2bdNtm+mwY8cOEFseSOCinCpUUO7cuVOPYWWERCdeZDP/LxMEi/naQ/7NteuuB263W5shRmEpCTpVaD9CxRGWb2uxgVgdTLTg4WKJYTThk0iCNyMtT3JRNoBCBVomL/Ld3d1oaGhQ9y/J1hipouRlxzELJyiWBuu3UYsVcl4Zdu3alfPaN62trWpyt6si7WNpBLG0Z88eEFt+XJEvZ+F7lEnCe4wRk4glWZbR1t7e7/hR8WQGsUQiVJZXbC4mmjtzscQwQWcpkdlwbITh9uzZoz4NRKkQnEmy4Szt27cPhJCshOAo2XLMwvlq2zb15pqm6xKOXDAIfr8/5wnRscoiJAtxFIJYHdjOSHgxnM7OTjQ1NSX0EEPsLhCLnfnZfdHQxFKg5Ar91+hiqaOjA0RRgsndAczgLDU1NQEIht36ojgK4PF4cl70lYslhunu7laLdCXSMoSB2XB+vx/7DxxQe2xlyXUJIVDEcc+ezD0R56bgZvaf9I8cOYKWo0chFQzS/VjKhWpYL9ehOJo3pUtOliBAdlWi/vBhJqdxJ9XfTxAgu8px8ODBnIf1U6F/4UZHyOtGhSbcE3u4WGJnxliqNDQ0AIIQ0VkijLTg4WKJYbq7uxMrGwA2wnAHDx4MTE3OfgiOQltPZKpXkpbcncWcrFw4S1TI6BqCC0C3yYZYEiAX6JPATnO7WCu6CfRNZE+sBIScXwFCSM7dv1QInzFmlirXmliKMmPMyGLp8OF6tblzhCK/tF9hrlvwcLHEMN3d3Ym1OgEbYbhcVO4OJ9OtJ/bs2aO2OYnSLiIjBByzbIZFvvrqKwCAXKi/WCJ2FxR7Ab766quc1fKRJAlf79ih1hxKZAJFAlCHKtciMBI0lyrR4qKKAXKwohFeuJEw1DIjHWhhRiU8Zykgnlgo3JgKPT09cLuPQXGw3a+QiyWG6e7pSdhZAgOlA7JZrDEamSziKMsy6ur2qmHGDLc56ffZ+eVoaWnJ2gX/q6++AkRrxoSvXDAYbW1tObsA7t27F709Pbo6Z6qzJDAnlggh2LZ9OxS7q1/Rv2hQt42VEg/JQEUDdWBYqgKdDnT6fD9nKXBMWZhenwq0WrySF61fYQkA4MCBA9kaUkS4WGKYnu7uhGbCAWzUWdKcpbzMF2uMRiaLOB4+fBg+nzerbVwo2ZzO3dnZib179wbylTJziaCO1ZdffpmR7cdDCzPq6ZxZbJDzy/D11zsgSZJ+202TpqYmtLrdSZWAUN0/l5rkb7BK3jQcpQREBP2XhcKG6aDVIgpPgrbYAIvdsPtHRRAVReEQuwsQrVwscSIjSRL8fr9WhTYeNAyXy4TMuro6Nb6cqBuWARRnESBaMiIqgvlKuSuLkI1Q3Ndffw1CiK71lcLJtVjSwow652TJBYPh83mZmkmmlYBwJXc85YJBaHW7tZlKRuHo0aPqpBitzpIDEC2GFRMUrcp1hBljsi0fR48ac/80sRStWKogQHYW4+DBgzmtws7FEqNoVWgTDsOpoipXVVxbW1vR2tqqhqhyiSBCdpZg3779kGVZ103nUixlM8mbChi5sCpjn6E4S0CsDk20ZBNCCLZs3RoIS+lcFiEgArdu3arrdtOBjiVZF40KyVwJ2lRpOnIEis0VnMUpCFBsLjQ15b5WTzocOXJErRkV4Z5A7C60t7cx3W4nGsGuD9HvHUp+Gbxeb05nxHGxxChaOC3BMBwtMZCrMFywX1ru8pUoSn4ZfD4vGhsbdd1usGxA9sNwxF4AYrFnTywJoq5tTvohCJALBqG+vj7rs3jq6+sDYamqDJRFYE9gbN26FcRiT/q7ScUyS8IvHl6vF61ud79K0IqjAG73MUOKCUrTkSMRXSUgWPmaheKNybJnzx51JlyMxut0hnW2a831hYslRgn2N0q8gSkRbTkLwwX7TuXYWULwCUXPUBwhBLt371ZnbOQizCgIkPPLcPDgwYxe8L1eL3bs2KE6WRneT6lAvRln211K1WlJBGLLh+IowtYvv9Td2UwFt9utlvRIIf9MySsFsTqwZcuWzAwuA2jFDcOK4hpZTABqSRhPV1fEOkRAMI/JaPvX1taGlpaWuJOCqNDPZXibiyVG0cJpSdywiGjNWRhu3759AHLTQDccKtjomPTA7Xajra0t5zP9FEXRdb/C2bVrFyRJykh9pXBy5cLQm3+mwoxSYRU8XV1MNKLdvHkzAEAqrE5+ZUGAVFCFhoYGw+Qt0dmV4R0ESGBa+uHDh7M+Jj2gLnm/3mkBqDjU203PNLT+V7y6ddTNp8vnAi6WGCU1Z8maM2dp3759gGgBcWa/zUk4Sr7+YimX+UoUesHIpBUddF0yl69EUfLLAdGaVbFECMHmLVtAbHkZq5UlF6nChAVH5osvvgAAyMVDUlqfrrdp0ybdxpRJ6DR0OSxZWA7MtKLvGw2aqxO1FhEjVa6TRauiH68wrNUO2VmCHTt25CzJm4slRqEOUaKz4QAAltw4S4QQ7Nu/H7KzOGNTzZMajzUPxOrQ9ck+l/lKFCULSd5UuEgZCFH1Q7RAclWirq4ua327Ghoa1DYuhfrnK1Go0My1WCKE4PPPPwexOlPuZSgVqWKJii7WCU5DDxXCtIaPUcUSdcyUKA+j1DkzmlhKpj+jUlAJj8eDQ4cOZXpYEcn9nY0TkaQTvBEMw2W7LsqRI0fQ29MDxZn7fCUAan5PXinq6+vh8/l02SQLzpLiLAEEMWNiSZZlbNu2XX0KD1Q9zjRy4WC1aGKWCjnSsJScSlgqQYjdBcVRhC1bt+Y0b+nQoUNobm6GVFSdsjAkjiIodhc++/zznE7bThTN4e4XhisEREtGQ9iZhIYPSRRniVgdIBa7ocKMsixj+9dfq8I2RnK3tnyOK+RzscQomrOURBiOCiu9BEKixK2TkQMUZwkURdHt4rF79271gmRLrAJyRhAtkJ0l2FNXl5EbV11dHXp6ujOS+BwN6sJkKxRH3R6pKHNiiW7f09WV1ebH4WzcuFEdS/Gw1DciCJCKh6KjvZ35PnGSJGHv3r2QnaX9He5ASZG6ujqmCoYmyoEDB9Q2S1HEEgQBirMYhw4dYmJiQSLs3bsXnq6uhF3sbF8rwuFiiVG0GU9J5SxZAGS/ijd9WmNKLAXGsn///rS31dPTg/r6ejV5PUOhm0RR8svQ29OTkUTObNRXCkd2VQKCmJULICEEX2zaBGLPj/qErhfUucplrs+GDRvUsRQPTWs7ckBs0e2xyqFDh+Dz+SC7Iru/cn45fD6fodwXQD1vDxw4ANlRFLPNkpJXAr/fb5gk7+D1JrEHF8VZDGJ15qyUBRdLjBJ0liyJryTmpjBltKTKXELFkh4l8vft2wdCCBM1pDJZnFK7eGVhJpxGoEXIzp07M14D5+DBg2h1u9WZYRkWvTTJm4b9sk1PTw+2bNkCOb88bTdUKhoKCCLWrVun0+gyA3W+os2sUly5n1GVCm63G52dnVF7p1FoErseD4jZQAuJJ3q9EQRIhVVoamrKiSDkYolRUnOWrKHrZglqEYfnCeQSRcfZLzRRPJdlAyiZahRMCMFXX32lVrWOMj05U8gFgyFJUsZvYjRJmSYtZxJiy4OcV4qtW7fC7/dn/PPC+fzzz+H3+yGVDE9/YxYbpMIq7Ny5M+sFRJNB6/cXpU0PbffCWqPjeGg9N+NMLslm/8h0kSQJmzZtUt2iJK43cuC7+/nnn2dqaFHhYolRqOBJLmdJdaGymbOkWsQHITuLmJgJRyG2PBCLXRexpCV3M1RDSu8LYn19vdqupiB7IThKtnIRaEgsk8ndfZGLqtHb26vN+Mkm1AWSSkbosj26nfXr1+uyvUywbds2wGKLWhhXyS8FRKvWK88oBGfiJla4MZdVrhNlx44d6O7uVl3LJJACIeXPPvssE8OKCTt3N04I6ThL2QzDtbW1oaurM2M1a1JGEKA4i3Dw0KG0k6H37t2rbi+ODZ4NiC0PxJanu1jSGstmMbmbQp2ATFbylmUZm7dsgeIoyppzRh2sbOctybKMtWvXqtXEdSp1QR2qtWvX6rI9vens7MT+/fshuSqiP7QJIqSCSuzbtw+dnZ3ZHWAa0KrV8R7W1GtDPvOJ+EBQ7EhJ1v8ijkIojiJ88cUXWU/UN4xYcrvdmDlzJoqKilBSUoJbbrklbm2Wuro6XHXVVaisrERRURGuvfbafuXgN23ahIsvvhglJSUoLy/HbbfdlrWaL7EIOkvJ5yxlMwxHa16kI5aeffbZiD/pojiL4ff51C7kKUIIQd3evZAdxUkJ10wi55WisbFR1wKkmljKZr5SAFogctu2bRmbnr5792515k2GZ8H1RXXphKyLpe3bt6OtrQ3+kuG65WYRRyHkvFJs/OyznPWfjMWmTZtACInrGsqF1Wph0hzlkqXCjh07VCGUQNNnyVWBI0eOMB0uBQKiW7Sk5PJKxcPg8Xiy3ibJMGJp5syZ2L59O5YvX453330Xn376KW677baoy3s8HkyfPh2CIKC2thZr1qyBz+fD5Zdfrl2QGxoaMG3aNBx//PHYsGEDli1bhu3bt+Omm27K0l5FRwulJSGWSA7CcHRmCXPOEoJjSqeIWUtLCzxdXUz0vKPQseiRvE756quv1GarOUrSlwoGw+PxZCw5VQvBZSFfScNqh+yqwPavv86qwFi9ejUAQCodqet2pdKR8Pt8WkkClqA5LPHCOvT9XOS8pMKxY8fQ1NQEyVWZkPBVApWwcxH6TZTm5mbs2bMHUuGQlPpPSqVqSDjbEw7YeFSOw44dO7Bs2TJ89tlnmDhxIgBgwYIFuOyyy/D4449jyJD+F8A1a9Zg//792Lx5M4qK1GnCixYtQmlpKWprazFt2jS8++67sNlseOaZZyAGpmQ+99xzOPXUU7Fnzx4cf/zx2dvJMKjgSc5Zyr5Y0pylNKZi33nnnZHfsOalvE0gOKZDhw5p502yaA2C89kRS7RR8N69ezF27Ni0t9fW1qbWZykelrPSCHLBIKDlG3z11VcYPXq07tvPdr4SRSqqhqXxKL766iucffbZGf88QghWrVoFYrEntK/OupUQe9uhOIvRe9yFMZeVSmvgaNiCVatW4YILLtBpxOkTrFTu0Ga8RUNxlYNYHeryhEDIcSmQeNB2IEqUpPVwaBL79u3bMWXKlIyNKx1oKDfVyQdyQRVgsWP16tW44447snYMDeEsrVu3DiUlJSE3vGnTpkEUxai1P7xeLwRBgMMRrAzqdDohiqL25OX1emG32zWhBAB5eeoNmi4TbdsdHR0hP3qjCR4hCWdJyL5Y0hpXOjNbtyYVlMCY6BhTgTodLDpLelUjpgmv0WYRZQOaK5WJ5Fufz4cvv/wKcl4ZiC07lckpcpbzlvbs2YPGxkZIxcMTcqXF3nZYuo9B7G2Pu6ySVwrFUYi1a9dmvfBtLA4cOKDuc9GQ+JNMBBFS0RA0NDQYovWJ1vQ5YbGk1i3LdaudWHzyyScA0ph8IIrwFw9DQ0NDVmf+GUIsNTU1YdCg0JPFarWirKwsajfsc845By6XC/fffz+6u7vh8Xgwd+5cyLKs1Wi46KKL0NTUhMceeww+nw+tra144IEHAMTu3vzII4+guLhY+xk+XIfpuWGkEobLhbNUX18PYrGBZKk9RjLo0VySiiUWa0jpFbIKiqXs5ytRiKMIxOrIyLTu7du3w+fzarWPsolcMAgQLVnrrbZy5UoAgFRWo//GBQH+0hp0d3czFcZas2YNAEAqSSzsSG/SsR6IWWHz5s2AaE2odxoAwGKFVDAIu3btgsfjyezgUqC1tRVbtmyFVDAYxJ56/S96ftPzPRvkVCw98MADEAQh5s/OnTtT2nZlZSWWLFmCpUuXoqCgAMXFxWhra8OZZ56pOUnjx4/HokWL8MQTTyA/Px9VVVUYNWoUBg8eHOI2hTNv3jy0t7drP5lo7KfWZhGSm44fcJayVdeFEIL6hgZVlLBoZ1sdIFZH+s5ShF5TOcVig+Io0NdZEgTIrgpdtpcSggC5YBAaGhrgdrt13TRN5s1GfaV+iFZIrkHYs2dPRhzovhBCsHLlJ2pdpDSrdkdDKhsFIOgOsMCqVatUxyjBti5S8TBAENX1GKa1tRV79+5V24Ek8dAsF1ZDUZScVbqOxaeffgpCFO08ShWpeBggWrFy5cqs9ULNac7SvffeGzeZevTo0aiqqkJzc3PI65Ikwe12o6oqel2Y6dOno66uDi0tLbBarSgpKUFVVVVITsQNN9yAG264AUeOHIHL5YIgCHjyySdj5k04HI6Q8F4m8Pl8yblKCOY3ZUsstbe3qw10S3MXvomH4ihEY2NjSvkJhBDs3x9oM8BQDSlALbp57NhhdHV1oaAg9anwkiRhx86dah5UCsmWeiK7BsHadgg7duzAeeedp9t2N23apIqxLLZx6YtcNATWzkZs3boV3/rWtzL2OXV1daivPwx/2eiMzdxU8suhOAqwevVq+P1+2Gy5PWcaGxuxc+dONXHbak9sJasDUmE1du7cicbGRlRXZ99xTARten1hciJfLqoGGjZj48aNmDx5ciaGljIfffQRADX/LS1EK/wlw3H48D7s3r0bJ554YvqDi/eRGf+EGFRWVmLMmDExf+x2O84991y0tbWFWNm1tbVQFAWTJk2K+zkVFRUoKSlBbW0tmpubccUVV/RbZvDgwSgoKMCrr74Kp9OJiy++WNd9TRZJkmL2AYpI4IaeLbFEQ6DEzpDrEoZiL4TP50vJrWhpaUFPTzdTPe8otEJ5uq7mvn374PN6tcTQXCIHZvLQpFY96O7uxo4dOyDnV+RMDNJyBZnOW/r444/Vz0vzqT0mggCpdBQ8Hk9OCgOGs2LFCgCAvzy5SQF0eXrzZhE620tOMhFaLhgEYnVg3bp1WXNdEuHIkSP48ssvIRVVpxWCo/jLjwMQPAcyDVuPy1EYO3YsZsyYgVmzZmHjxo1Ys2YNZs+ejeuuu06bCVdfX48xY8aETGtduHAh1q9fj7q6Orz44ov4wQ9+gDlz5uCkk07Slnn66aexadMmfPPNN3jmmWcwe/ZsPPLIIygpKcn2boYgSZKWsJ0wAbGUra7TVCwpWW6PkQx0bNFy22JBE0CZLIsQKJCZbpIqFSZUqOQSmpeh57Tnbdu2QZblrNZXCkcJCLVM1vYhhKhiKYMhOIo/IMaymS8SCUIIlq9YAYiWpJ0KqXQkIFqwfPlypgQFRZIkbNiwAYqjKPnrTyAkeeTIEd1C9XqgCduy43TZnlw0DMTqwIoVH2XlnmcIsQQAixcvxpgxYzB16lRcdtllmDJlCl544QXtfb/fj127doUU6tu1axeuvPJKjB07Fr/73e/w4IMP4vHHHw/Z7saNG3HxxRfjlFNOwQsvvIDnn38ed999d9b2KxqqWEru8JAcOUssiyVarTm8GGkiBMVSiZ5D0gU6pnRrLWlTkxNNIM0kFhvkvFLs2LFTt+KUWrPOLJcMCEEUIRUMxv79+9Ha2pqRj9i9ezcaGhrgLx6e8eKpaiiuEKtWrc56H8q+7Nq1CwcPHIC/eETyrqHFDn/xCBw4cIDJxrqbN29W24GkWFiUTsv/9NNP9R5aShBC8OGHH6YkbKMiivCXjYbbfSwrs00NI5bKysrw0ksvobOzE+3t7fjnP/8ZkqtRU1MDQgguvPBC7bX58+ejqakJPp8P33zzDX7xi1/0y1v517/+hWPHjsHr9WLr1q348Y9/nK1dionf70/+SxIQS9kqA0/zyIidXbGkBKrehue8JQLbBTfVsgh0jKmyc+dOtRglI/uouCrQ09Ot26SJrVu3AoKY07IIQLD/XaaSbmkIzl+mf42qfggC/KWj0NPTndMCle+99x4AwF+ZWr6Kv/KEkO2wRG1tLYCgi5csUkA01378MRPO2c6dO3HgwAH4S0YmnluWAP5ytRbi+++/r9s2o2EYsTTQkGU5+aRiMbthOCpAlATK8OcK2iIglZYnwVYu7NWQIlYniMWelljq7u7GwYMHIeeXMzObkc7IS3UWbF+6u7uxa9cudZs5Tl6XMiiWCCHqTdFih5zhEBxFKldv4lSkZZvu7m4sX7ECir0g5arsctEQEHsBVqz4iKkWLj6fD5+uWgXFXpC642uxwV88HAcPHGAiFEfFjL/iBF23q7gqIOeVYNWqVRnv98fFEqNIkgSS7OHJsrN09OhR1fK36PekoDdKwPVK1Vkitvyc32gjEmgUfPhwfcpPjrt371b7aeWyZEAYNG9JD7H09ddfq/lKOZoF1xclvwIQrRkRSzt37sSRpia1flCSM2hTRckrg+Isxtq1a7PauJtSW1uL3p4e1VVKVegLInwVJ6Cnp5upRO8NGzaofQzLRqX1EEMT/ZcvX67X0FKit7cXH330EYjdpX+tM0GAv+IE+P3+jCd6c7HEKKqzlORU9yyLpZaWFii2fGZciYhY7IBoSbqxpCRJOHLkCGSW6iuFoTgK4fN5U26aSbuTx2sRkU2UvFJAELVO6+mgNQdmQCypeUuDMtLxPhiCy+AsuHAEAf6yUejt7Y3aRSFTEELwxptvAoIIf0V6U8b9lScBgog333yTiXAVACxbtgxAMMSUKlLJcBCrAx988GHW7gmR+OSTT+DxeOCrOCEjJVik8uMBQcR7772X0WPIxRKjpBSGgypaMtW5vS+yLMPd2gpFhymgGUUQoNjy0NLSktRqR48ehSzLbBWjDCPdCuV79uwBAHVaPSuIFsh5JdizZ0/a4eQvv/wSgJDzfCWKXDAYhBBdq5QriqKG4KwOyHGayOqNVJqbUNzWrVuxb+9e+Etr0p6CTuz58JfWYO/evYHzJbccO3YM69atg+yqSL8fpWiBv+w4uN3HclrmQcstS1PYRoPY8uAvGYE9e/ZoD4CZgIslRpEVJYUE7+yJpba2NhBFAbGl1+w2Gyi2fBxzu5N66qACRGFaLKXX+2737t1qqxrG9lHJL0dvb29a+ViSJGH79q8h55cyEyam/e/0vCl//fXXaDl6VE2cTbYuW5oo+aWQ80qwdu26rOb8vPnmmwAA3+BxumyPbueNN97QZXvp8MEHH0BRFN2ERa6T2A8cOKDWVioeqs1MzgT+SrUc0Lvvvpuxz+BiiVGIooA6RYmTPbFEp0AbQSwRWx5kSUJXV1fC65i9LILP58OBAwcg55UxF0aV88sAAHv37k15G3V1dWo/uBz2uwtHbXIq6Fp0U2tKmolecAkgldbA5/NmLRR3+PBhrFq1CrKrEkocx9BZtxL529+Gs25lzOUUVyVkVyVWrVqV9uzSdJBlGW+/8w5gseo2q1HJL4fsqsCaNWtTuk6kCxUv/oqT4iyZHnLRECiOAixfsSKkfJCecLHEKAohSFosZdlZAgBiNYBYCowxmRo39MLCmuvSF5q8nmoNKVmW07f6M4CSl75Y0poDs1A/imKxQc4rw46dO3XJIVEUBStXfqKG4JJsiaEXNBSXrV5xr732Gggh8FWdEndZsbcdlu5jEHvbYy8oCPBVnQJCCJYsWaLTSJNnw4YNONLUBF/Z8bpOr/cNGgtCFCxdulS3bSaC1+vFsmXLQGx5WvPijCEI8FechN6enowl63OxxChEUUCSfuBXV8hGoqImlmzOjH9WutAxJiOWNGeJ4RpStCxCKmKJChEqTFhCDgi4urq6lLcRrEzORr4SRS6ohM/rTWvfKDt37sTRo83wl4zIegiOouSVZG1WXGtrK95ftgyKowhSqb43X6l0BBRHIf7v/fe1a1u2eeuttwAA/sFjdd2uVDYKxOrE0qVLs1pE9JNPPkFnZyd8FSdm5fz0BxLI33nnnYxsP6eNdDnRUeVOss5SYN0siKX2dvVpjVgNIJasatPjZLq+07pMxJZ+Avuzzz4b8fWf3n1vehsWLSC2vJRqSNHaK0oee84SrE4QWz7279+f8iZ27NgBYnUw5wyqPfh2YufOnSFtl1Jh1apVAHRoSpoOggB/aQ3Exq34/PPPMWXKlIx91BtvvAG/zwffyIn6z6oSRPiqToZ4YB1ef/113HrrrfpuPw579+7FZ599BqmwWv/vpGiFr/JEtDd+iRUrVuA73/mOvtuPAnWyUi0amizEng9/yXDs3r0bO3fuxJgxY3TdPneWWIWQFHJJsucsUeFBhQjLEEvyYqmlpUXNx8rRE3uiKLZ8NDc3J33MaZsUmcEmwQAg5xWjsbExpSfhzs5O1NfXq/WjGMvHUgI1rdJtsUEIwaefrlJDeykWZdQLqXQkAGD16tUZ+4yuri51er8tD/6K9KbUR8NfcQKILQ9vvvUWPB5PRj4jGjT856s6OSPb9w8aBwiiFsbMNPv378dXX30FqXhY0g8sieaaRcJfqQqkTCR6c2eJkxI0WZoKEZahgi7R+jaEEBw9ehSKTZ/K5HfeeWfkN3TI91LsLnjbjqGrqwuFhYlflPbv36+KQUbFruIsAeloxMGDB3HCCclV/aXTh5nKVwqgOIsAiy1tsXTgwAHU1x9WaytlqRBlNJT8cih2F9asWQNJkmC16n9befvtt9Hd3Q3vsLMy1/tOtMI7+GQIhz/Df/7zH8ycOTMznxPGsWPHsHz5cijOYsjFwzLyGcSeD3/ZaBw4sAcbN27EpEmTMvI5lKCrlLx7SnPNUkFN9C7EihUrcMcdd8Dl0q+7BNuPzRxmocKD6JiImCmoWEp0NlxPTw96e3vTruGSDWiYMJnClD6fD41NTZAZ6QcXCSXgeNFmxslAC1oq+ewU29QQREh5Zdi3b19a+SNr1qwBgMwnziaCIEAqGYnOzk4tsV5Pent78dprrwFWB/yD9A2thOMfNAaw2rFkyZKsVSZ/4403IEmS6ipl0AmlrtXLL7+csc8A1MTuDz74AMSWrzX0zRqCAH/liejt7dX66+kFF0umInsVaOn0TMJIDZuYBNqVJDql1O12AwAUg5RFAJJLXm9sbARRFGaa50aC1pBKZSp3sNgme8nrAKDkl0FRlJSEIGXdunWqSMmQE5Es9Ka4bt063bf97rvvor29Hd5B4zLfeshig3fQOLS1tWWlNlFXVxf+85//gNjy067YHQ8lvwxS8XBs2bJF1/IV4axevRpdXV0Zq9gdDzXRW9D9+HGxxCqCoOYtJQOhq2Y+T0MTHpmyxHWEiOoFNtE8BK2GlBHKIgRm+iUzg4cKECpIWIQ2L05FLNXV1YFY7CCMzmRUAiKOirpkaW9vx/btX0MqGMxMGFUurAJEq+5iyefzqU6IxaZbEcq4nzl4PGCx4aWXX4bP58voZy1dulQNLw4en1Q4NdW8Hl+1WnIhk+5SsLaSvk1zE4XY8uEvHo6dO3em/B2LBBdLjBJI1U5yrcAcumyJJdHKXAJtJEjgaTTRKsOGKouQQg0pWvGbONkVS8TuAgQx6erkfr9frSGVV8rsuSmnWUfq888/ByFKxvJbUkK0wF80BAcOHNDKbujBhx9+iGPHjsFbOSZ7wtDqgLdyDI61tODDDz/M2Md4vV68tmQJYLHDPyi53J6Ea0iFIRdWQSoYhFWrVmmTPPSksbERmzdvhlRUndPrC82Vev/993XbJhdLjCKIYtL1uyliFmZw9fb2aiKEeQLuV6I5IsGZfkYQS8nP9AtWJ2drWn0IggjFXoDGJG+89fX1arFNRmf5AcF8rFRvVl988QUAQCrObi+4eMiB8dDxpYskSXjppZdUIZahWWLR8FepTs9LL7+cdo/CaCxbtgytbje8g8ZmtSWPr/pUAFD/tjrzwQcfAMhcH7hEkYuHgtjysHzFCvj9fl22ycUSo4gpheGy5yz5fD4QIbezcBJGEAEICSdsGqosQpIz/QBjtHIB1PG1ut1JJUJTAaI4SzI0Kh2w2KDYXSmJJUIIPvvscxCrk7mColKghMHnn3+uy/Y++eQTNDQ0wBeY0p9NiC0fvvIT0FBfn5Hq5JIk4eWXXwFEK/xZCi9S5OLhkPNKsWLFCl1boBBCVLFksUEqGanbdlNCEOEvOw4d7e26teLhYolRRNGSglhS25xYLJkXMb29XpAcT1lOGEEAREvC+QeaWDJA8joVS7RIaCI0NTWp6zG+f0oKve9o0rSSx27yOgAozmIcOXIk6RlX9fX1OHq0GVJRdcphxvxtb8G1+WW4Nr8MsVudzCB2u+Ha/DLyt72V0jYBgDiKoNgL8MWmTWnX8iGEqHk1gVYkucBXfQogCHjppZd0r030ySefoKmpEb6KE7PfX1MQ4Ks+FbIs49VXX9Vts9u2bUNjYyN8pTWAJfe5rLQel16hVC6WGEUVPMn2eFO/0NkIw0mSPyczHVKFiJaE7djgTD8DOEsBwZNM80i1hpQRyiKoNVJaWloSXqexsREA28nrQHB8yeb3fPnllwAAubA65c8W/L0QpR6IUg8ErVcAUX/3pzFdXhAgF1aho709rZl+ALBp0ybs2bMH/tJROavCThyF8JfWYM+ePdi8ebN+2w0RguN1224ySGWjoDgK8N577yX1oBULOlVfKj9Ol+2li5JfBjmvFOvWr9elua5x7nYDDNEiJu0sCYHls+EsSZJkKLEECAk3L9VmzRkhJyuQj5XoxcDr9aKzs9MYNaTsyYulhoYGAIK2LqvQfDF1vInz1VdfAQDkwsG6j0kP6LjoOFMl0xWtE4W6Wq+99ppu2/ziiy9yLgQhiPANPhlerxdvv/122puTZRkrV64EseWpMyMZQSobBb/Pp9UlSwcj3e0GFFarVQurJUxg+UxU0A1HkmRDiSUiiAknatJZc4ZIYBcEwGJPWCzR4pVGcJZonatket81NDRAsefnvKp1POhNkjphifLlV1+BWB3M5mTJBemLpQMHDmD9+vWQCqu09jC5QnFVQCqswvr163WbPUZDX3Qaf67wV5wAYnXgjTfeSLvB7pdffonW1lb4S0cydV/wl40GAKxcuTLtbbGzV5wQrBYrBIbFEiEKs1OzIyIICecdaCUGDFBDClBDjInmvmg1pAxRcFMVdInWkJJlGS0tLVAYra/UF5qP1dzcnPA6nZ2dqD98GHI+ez3vKIqzGMRiT6udC+0a7x+cmxBVOHQctIVHOhw4cCDYMDfXFeYtNvgqT0J7ezs+/vjjtDalVZTPZVPnCBBnEeS8Umz87LO0K7JzscQoVquFaWfJzPh8PvXpiNEbUjgkieR1rYaUEcoiBOpcJZpT0dbWBkVRjBFiTKFNjVaZ3MVgGxeKIEDOL8OBgwdTujn19vZi2QcfgNhz0CojClLJcBBbPt5ftiztG+5bb6kJ9NmeARcNf+UYQBDUJsUpJrETQrB27VrAYodcwE4IjiKVjIDf50s774yLJUax2WxJO0sCF0u64PP5DOMqAQARrAnb6IYSS4GZfok6SzS3iRggxKgKQSGpEGOw511uQ1PxUPLLQRQFdXV1Sa/78ccfw9PVBV/FSeyEcwQRvsoT4enqSsuB8Xg8ASFYwIwQJI4C+EtG4ptvvkm5BcqhQ4fQ0NAAf9FQIMXJRZmaoQkEW/GsXbs2re0wcjZywkkpZ0lRc3Jstizk2qRSByqXJDFWv98PwsqFOhFEEb4EZ/oFGyCzP9MPohUQrQnXkKKiygj5WBDE/9/enUdHVV9xAP++yawhIZONLJCVoqzRABJDrLUlPW51QQ+KxhZEsWwFlCooYmgVcaG1oBUtpUAFTNWSVFxAwJAWTkwkMoEoQtBogmRBMBsBkpl3+8fkvTAhmbzZknnxfs7haN42v9+8We78lvsD6YwuZV6vqKgAANgCQ31VKq+Q1uSTyuuKjqSG7i2V4asvXSnJoifT0Pfu3Yvz586hNdKPAkEAbYNGAAA++OADt86X8mpZze5nlPfZDE3Yx52R1uBxslT/uWPMgV6vhyC6mDm2F1uWtAFaVQVLAkjx8yKKKhuPBQGiTVlg3dzcbP8fP8+xJKEAfUeZeyB116kiEAQgBhhcyrxeVVVlD7L8OfM6IC/Q7Oq6ft9//z1KS0thDY4GuZkw1VdfumQIgjU4GhaLxaWu04t1BILuLZjrq0DQFhwNUR+E/Px8twZ6d6Sz8L8uOACAoIE1KAonT550qSW3Mw6W/JROpwPItWBJ6MWWJffyQPUhIsUpFURRBLm92EwfEDQQFQbWUloENSTcBAAxQKd4AWS5BUolwRJpDfY0Dgp/dFRWVcFmCParVomuSMFSVVWVS+fl5+eDiGBtn8Hkb6xhySAit7riqqurcejQIVgHxrqd1sKX+bHawoeipaUF+/btc+lUIoLFYoGoD/LrIF4K5KTAzh3+/a77EdPr9faWG1e64tqDK73e91+EWm0AIKonWBLI5lKwpLqWJYVfuB0JN1WQFgEAAnSK0yLIXYwqCQSh1cNmsyla4Pns2bNobGgA+XmyTQCA1gDSGlxeBNn+RS343Ywqib1cAv73v/+5fK40db3NTxI2dia1drkaCFZXV6O+vh62oEG+KJbXSMFSWVmZ29fgYMlPGQztv45d6YprP1Y+14cMBgMEF1u++pRoU/y89EYGdO8i+1qCCsiz5vw8D5GENFrFM/2kLgS1BILkwgLP0uB10c+TbUpEXSC+/155d1VLSwvKPv8ctgER8ixIf0M6I2wDIlD2+ecuZ4QuLCwEIMBqjvdN4TxExhCIRjMOHDig+P0GqGSGJtoXrxY0bk06kKhnys+PjNQ6JIg2xR/+gmjPUN0b3XAGgwGCqHzx1j5FIkCiS0GkoKLxWAApXjxZ+iAktcz2EwIgiiKsVmuPY87kad1eCgRfffXVLrfPmr/IK9eX7oGS6ejyTD8VpEUA7NnXmxtO4MKFC4redxaLBTarFdZBg3uhdO6zhgxGwMlTKC0tRXp6uqJzGhsbcfhwGaxBg/y6i9hqHoLzNWUoLS3FVVddpegcKfjwt0WdL6HRwmYMwfHjx0Gk/PPS4RI+KBbzArkrTVS2RIf9WHtLj8nk+4SDRqNRDs78nostbvbuOhUFS0QQFI5jkX81CmppWbKXU8mvXbllSS2BoAstS2fO2Af0qiGZKNCRfV3pYGiLxQIAsA2M9VWRvEIqnys5ez777DMQibB5MFusN1hD7FPsP/30U8XnSDMexUA/D5YAiKZQtLS0uJQI9mIq+VT58TEa25uiXeiGk4KX3uiGM5lMgK3NPq7Kz8f3CDb7tPrAQGW/yt1K29CXSIROr6w1UZTGmfn5PetgL6eoYHxcxzHeqducOXO63qH1UsAiKK+b2gbmS7MtlQ7Ot+eQEuzZyf2YrT3rttT9pMSXX35pPzfIP9fzk9gGRAKCIJdXiZqaGkCjVUXeNmk9xtraWkRFuX4vuGXJT0kBj0utN+3HyoGWD9lbr8i1MVV9xcVgSafTuZ62oQ8Jog0GhYP63c3S22faAwol5ZaPUVkgqKRuahuYL5VTyeB1IsLx48dhMw4EAvz893uADqJxoNydo4R96RdBDrT8VoAWNmMojh07pngdzdraOtj0Qap4z5EbSwxdzM9fmT9eUsAjiMqSDdqP7b3ZcAMG2AeaCrZWkJ9/wAk2exeO0mBJr9fbg0AVtJoBAMimeJyat1uWfD2ux72WJXVRUm55cWeNSoKl9nIqGQh9+vRpNDU1QQxN8nWxvMJmCkPjD9/g9OnTiIhw3hJGRPbgwxQCqCDQtQ2IwPnvj+HEiRNISEhweuyFCxfQ0FAPGujf48wk0uSI2tpat87nliU/1dEN50rLkj2w6o0xS8HB9iZNKRDxZ1IZpTL3xP7ck8t5rvqKINoU33N5pp9qWpjs5VQyQ1FODaGWurWXU0lKi44gVyUf2e3lVBIIyjP93ExE2dukfEJSuZ1pbm7G2bNnIaoh5QPsC88C7d1rPZC7hrXq6BomF7uGO/PvJoEfMenLT7ApD5akY3s1WLK6nvG1t0llHDhQ2QeW1AIl2NpUMVhYsLUqvucOAYUXWpd8Pq6nfeyYkoBCbl1TSxeqW3nRVBIIulBOackXNYx7AQCxvZxK1izsGJivjlmM0sB8qdzOSLM41dLaKbXsubsYsv9/E/xIdbQsudIN13tjluTAw8NgiXRGOQ+4YD0PAQSCANIavZZvRbDZyxgUpOyXqxx42NoAf599JNoAEt0IlkSooWFZcCFYktNtkE0VIUVvZtzvzPfdp8rJizv7aX6lzqRyKlnXT3UpH9qDOiWtZh2pOtQRRkg/fJWMo+uKOmr5IyS3brjYDSdoNL0yGy4kxL6kgWD1bJHDltGT5f8P/Pw/CGg5DTEwDC2jbvPouheTlgKQytwTKagSbG1+/6UrdTEqDQTlVgyyQRVvfxcCCjlgVE1KC+U/bi7Ou6YGroyf7EiUqoLXIyDn8eqP6SykgfkurRGngmGddp4VVB138EeooxvOhZYlWxtMRqNbCbdcFRpqX/lcY3UvSu9NQnsZw8KU5QLpGI/l/12MsLozHssehBP8N0GeRBCt0OsNisYsyUGuCrqGgY5AV5os4Yw8OcGFzwNnfN596kLdHFs7VaB9rJmShbnVNo7OnZZc1XR7ezgBSjXB0ooVK/D+++/DYrFAr9cr6i8mImRnZ2PdunWor69HRkYG1q5di2HDhsnHnDlzBr/73e+wfft2aDQa3HnnnVi9erXiX+q+4s6Ho2BrQ2Bw7zT3ms1m+2N6soBjL5HKKJW5J1IXoxq+dKWATul4LLkVw4WxcH1KtMJoUtY909Ei6P+TDgBAsLbCaDIp+tJVXd3aP7eUBEty/VUTLNnLqeS+SUG+4KU2ap93n5LyCRXebu30dd0E8iwPoWqCpdbWVkyZMgXp6elYv369onNeeOEFrFmzBps2bUJSUhKWLVuG66+/Hl988YX8pZGVlYXq6mrs2rULbW1tuP/++/HQQw9h69atvqxOjy4eZKyUILZhwIBQXxXJgdRKI7S5tkZSXxDaWhCg1SpufekIljwPBH09Jksqo9K6dXTv+n8XI9D+A8Ck7IeLmiYdAPZ7N9Cs7L6psW6AstdlRyu6dwJBn3/ptpdTyTjBjh8n6ghypR/nSrqGO8Z2qqNuglX5feuKaoKlP/zhDwCAjRs3KjqeiPCXv/wFTz75JG67zT7+5Z///CeioqKQl5eHqVOn4siRI9ixYwc+/fRTjB8/HgDw8ssv46abbsKqVasQG9t3qfflPEauDPC2tSnOJeSp0NBQaDQaCK3+HyxpWlsQER6ueIFcb7aa+XpMlqbN3sUodYv2RG1dVRpbq+JAsCOA9/+uYRBBYz2P8PA4RYdL+Xw0be5Ne+5tmrYWaLVaReMEpWzKmgsqqduFZgBQlAU6Ojra4RxP+br7VNNqX+9TKrczQUFBMBqNsHnpO8DXdZN+2EdGRrp1vmqCJVdVVFSgpqYGmZmZ8raQkBCkpaWhsLAQU6dORWFhIcxmsxwoAUBmZiY0Gg2KioowefLkri6NCxcuOAyAa2xs9Hr5paBH01wHfVXHWj1C61n5vxdvBwCIVkXN3t6g0WgQHh6O2h8aoD9Z6pVrSl9yQts5r10TsH9wR0Q4T7B2MelLN6DxJHReTLh5cf10NYe9ck1toz0fiqvjsXSnj0PT0vP0YGecBfKC2AZdtWd1FECAG8GStvEkqMaz3C891s3D+yeQCIhWxfdN+oAPaKz2+L3RU9288d7TnG9AZGSkoh8o0hdzQFM1dDVlHj2uIFq7/dIVNDqPrw8AAU3295ySYCk8PBxarRbi2VOev2Z8/JoEAG39CQBATExMz+URBERGRuLcyVqv3Ddn+7x53zhY6kRKqtX5BR0VFSXvq6mpwaBBgxz2a7VahIWFOU3KtXLlSrmly1cCAwMxMCQEjQ0NCOjiTaCxnoehi+1KXuTekpCQgFOnDsDwXYlXr6tpa/H6NXvKRnuxyMhI++yr5lpom93L9uqMpq0Fxs6BroeUtoJKX0y6019Bh6+8WoaLCaIVxhPeqaOSX7mAvfXFYDDgwtlTCDh7yiuP3RVBtHrt/g0Zomxx1dDQUAQHB6Op6TQCWpQtTusOQbR67b0XHx+v6LiQkBCYzWbU1/u6bm0wVhV75Vpms1lRq5lGo0FiYhKOHy/3+nv+Yt58Tep0OsWfJ/Hx8aiqqvLa89oVb943QPl7rrM+DZaWLFmC559/3ukxR44cwfDhw3upRMo8/vjjeOSRR+S/GxsbERenrDldqYCAAPxz06ZLgrb169fLCwE+8MADDvsEQUBycrJXy+FMdnZ2+wKY3rF582bU1dVh0KBBuO+++7x2XUEQXHoNBQYGYt26dYqy2LoiJycHp06dQmRkJKZOneq164aFhSn+cLvqqqvwyiuvuJ3F9mIvvfQSmpvt3Qvnz5+HKIrQaDQwGo0ICgrCww8/7PFjCIKA0aNHKzrWYDBg3bp1qK6u9vhxe6NuGo0GY8aMUXzsunXr8N1333n8uMuXL0dTU1OX+4KDg7F8+XKPHwMALrvsMkXHCYKAV199FZWVlR4/5jPPPCPft86CgoLw5JNPevwYgD1IUDrr+LnnVuKrrzz/YfL00087rduyZcs8fgzA3qCgdILT4sWL8cUXX3j8mL1138LCwjB4sHvLs/RpsLRo0SJMnz7d6THufvlLv0Zra2sdWltqa2tx5ZVXysd0XlTParXizJkzTn/NGgyGXsllZDabL5nB9eKLL/r8cZUKDg7G2LFjvXY9b17LU4mJiUhMTPTqNa+++mqvXs8dGo1GcfDRk5ycHK9cx5vi4+MVt2g44491i46OVtzK5kxERES3M7nMZjPGjRvn8WO4KjY21itjROVW4S6YzeY+eQ9GRET0uIacEj3VLS0tzePHcNXAgQO98pz6433rrE+DpcjISLf7D3uSlJSE6Oho7NmzRw6OGhsbUVRUhNmzZwMA0tPTUV9fj5KSEvkD4uOPP4Yoin3ywmOMMV/bsGFDXxfBZ7hu6qSGuvn/egftKisrYbFYUFlZCZvNBovFAovF4tB0N3z4cOTm5gKwN+0uXLgQzzzzDN59910cPnwYv/nNbxAbG4vbb78dADBixAjccMMNmDlzJoqLi7F//37MmzcPU6dO7dOZcIwxxhjzH6oZ4P3UU09h06ZN8t+pqakAgPz8fFx33XUAgKNHj6KhoUE+5rHHHsPZs2fx0EMPob6+Htdccw127NjhkENiy5YtmDdvHiZNmiQnpVyzZk3vVIoxxhhjfk8gUkkedj/W2NiIkJAQNDQ0KM6kzBhjjLG+pfT7WzXdcIwxxhhjfYGDJcYYY4wxJzhYYowxxhhzgoMlxhhjjDEnOFhijDHGGHOCgyXGGGOMMSc4WGKMMcYYc4KDJcYYY4wxJzhYYowxxhhzQjXLnfgzKQl6Y2NjH5eEMcYYY0pJ39s9LWbCwZIXNDU1AQDi4uL6uCSMMcYYc1VTUxNCQkK63c9rw3mBKIo4efIkgoODIQiCTx+rsbERcXFxqKqq6nfr0PXnugH9u35cN3XiuqkT1817iAhNTU2IjY2FRtP9yCRuWfICjUaDIUOG9OpjDhw4sN+9SST9uW5A/64f102duG7qxHXzDmctShIe4M0YY4wx5gQHS4wxxhhjTnCwpDIGgwHZ2dkwGAx9XRSv6891A/p3/bhu6sR1UyeuW+/jAd6MMcYYY05wyxJjjDHGmBMcLDHGGGOMOcHBEmOMMcaYExwsMcYYY4w5wcFSH/vvf/+LW265BbGxsRAEAXl5eQ77iQhPPfUUYmJiYDKZkJmZifLy8h6v+9e//hWJiYkwGo1IS0tDcXGxj2rQtZUrV+Kqq65CcHAwBg0ahNtvvx1Hjx51OOb8+fOYO3cuwsPDERQUhDvvvBO1tbVOr+vu8+FNa9euRUpKipw0LT09HR9++KG8X6316spzzz0HQRCwcOFCeZua67d8+XIIguDwb/jw4fJ+NdcNAL777jvcd999CA8Ph8lkwpgxY3DgwAGPy9rXnyeJiYmX3DdBEDB37lwA6r5vNpsNy5YtQ1JSEkwmE4YOHYqnn37aYa0ytd43wL6MyMKFC5GQkACTyYSJEyfi008/lferpm7E+tQHH3xAS5cupW3bthEAys3Nddj/3HPPUUhICOXl5VFpaSndeuutlJSUROfOnev2mjk5OaTX6+kf//gHff755zRz5kwym81UW1vr49p0uP7662nDhg1UVlZGFouFbrrpJoqPj6fm5mb5mFmzZlFcXBzt2bOHDhw4QFdffTVNnDjR6XXdeT687d1336X333+fjh07RkePHqUnnniCdDodlZWVEZF669VZcXExJSYmUkpKCi1YsEDerub6ZWdn06hRo6i6ulr+d+rUKXm/mut25swZSkhIoOnTp1NRURF9/fXXtHPnTjp+/LhHZfWHz5O6ujqHe7Zr1y4CQPn5+USk7vu2YsUKCg8Pp/fee48qKiro7bffpqCgIFq9erVHZfWH+0ZEdNddd9HIkSOpoKCAysvLKTs7mwYOHEgnTpwgIvXUjYMlP9I5WBJFkaKjo+nFF1+Ut9XX15PBYKA333yz2+tMmDCB5s6dK/9ts9koNjaWVq5c6ZNyK1FXV0cAqKCggIjs9dDpdPT222/Lxxw5coQAUGFhYZfXcPf56A2hoaH097//vd/Uq6mpiYYNG0a7du2in/3sZ3KwpPb6ZWdn0xVXXNHlPrXXbfHixXTNNdd0u78/fZ4sWLCAhg4dSqIoqv6+3XzzzTRjxgyHbXfccQdlZWV5VFZ/uG8tLS0UEBBA7733nsP2sWPH0tKlS1VVN+6G82MVFRWoqalBZmamvC0kJARpaWkoLCzs8pzW1laUlJQ4nKPRaJCZmdntOb2hoaEBABAWFgYAKCkpQVtbm0M5hw8fjvj4+G7L6c7z4Ws2mw05OTk4e/Ys0tPT+0295s6di5tvvtmhTED/uG/l5eWIjY1FcnIysrKyUFlZCUD9dXv33Xcxfvx4TJkyBYMGDUJqairWrVvnUVn98fOktbUVmzdvxowZMyAIgurv28SJE7Fnzx4cO3YMAFBaWop9+/bhxhtvdLus/nLfrFYrbDYbjEajw3aTyYR9+/apqm4cLPmxmpoaAEBUVJTD9qioKHlfZ99//z1sNptL5/iaKIpYuHAhMjIyMHr0aAD2uun1epjNZodjnZXTnefDVw4fPoygoCAYDAbMmjULubm5GDlypOrrBQA5OTn47LPPsHLlykv2qb1+aWlp2LhxI3bs2IG1a9eioqICP/3pT9HU1KT6un399ddYu3Ythg0bhp07d2L27NmYP38+Nm3a5HZZ/fHzJC8vD/X19Zg+fToA9b8mlyxZgqlTp2L48OHQ6XRITU3FwoULkZWV5XZZ/eW+BQcHIz09HU8//TROnjwJm82GzZs3o7CwENXV1aqqm9ZnV2as3dy5c1FWVoZ9+/b1dVG85vLLL4fFYkFDQwPeeecdTJs2DQUFBX1dLI9VVVVhwYIF2LVr1yW/BvsD6dc6AKSkpCAtLQ0JCQl46623YDKZ+rBknhNFEePHj8ezzz4LAEhNTUVZWRlee+01TJs2rY9L5z3r16/HjTfeiNjY2L4uile89dZb2LJlC7Zu3YpRo0bBYrFg4cKFiI2N7Rf37Y033sCMGTMwePBgBAQEYOzYsbjnnntQUlLS10VzCbcs+bHo6GgAuGRWR21trbyvs4iICAQEBLh0ji/NmzcP7733HvLz8zFkyBB5e3R0NFpbW1FfX+9wvLNyuvN8+Iper8dPfvITjBs3DitXrsQVV1yB1atXq75eJSUlqKurw9ixY6HVaqHValFQUIA1a9ZAq9UiKipK1fXrzGw247LLLsPx48dVf+9iYmIwcuRIh20jRoyQuxn7w+fJt99+i927d+PBBx+Ut6n9vj366KNy69KYMWPw61//Gg8//LDcsqv2+zZ06FAUFBSgubkZVVVVKC4uRltbG5KTk1VVNw6W/FhSUhKio6OxZ88eeVtjYyOKioqQnp7e5Tl6vR7jxo1zOEcURezZs6fbc3yBiDBv3jzk5ubi448/RlJSksP+cePGQafTOZTz6NGjqKys7Lac7jwfvUUURVy4cEH19Zo0aRIOHz4Mi8Ui/xs/fjyysrLk/1dz/Tprbm7GV199hZiYGNXfu4yMjEvScxw7dgwJCQlul9VfPk8kGzZswKBBg3DzzTfL29R+31paWqDROH4VBwQEQBRFt8vqb/cNAAYMGICYmBj88MMP2LlzJ2677TZ11c1nQ8eZIk1NTXTw4EE6ePAgAaA///nPdPDgQfr222+JyD6t0mw203/+8x86dOgQ3XbbbZdMq/zFL35BL7/8svx3Tk4OGQwG2rhxI33xxRf00EMPkdlsppqaml6r1+zZsykkJIT27t3rMOW3paVFPmbWrFkUHx9PH3/8MR04cIDS09MpPT3d4TqXX345bdu2Tf5byfPha0uWLKGCggKqqKigQ4cO0ZIlS0gQBProo49UXa/uXDwbjkjd9Vu0aBHt3buXKioqaP/+/ZSZmUkRERFUV1en+roVFxeTVqulFStWUHl5OW3ZsoUCAwNp8+bNLpXVHz9PiOwznuLj42nx4sWX7FPzfZs2bRoNHjxYTh2wbds2ioiIoMcee8ylsvrrfduxYwd9+OGH9PXXX9NHH31EV1xxBaWlpVFra6uq6sbBUh/Lz88nAJf8mzZtGhHZp40uW7aMoqKiyGAw0KRJk+jo0aMO10hISKDs7GyHbS+//DLFx8eTXq+nCRMm0CeffNJLNbLrqk4AaMOGDfIx586dozlz5lBoaCgFBgbS5MmTqbq6+pLrXHyOkufD12bMmEEJCQmk1+spMjKSJk2aJAdKROqtV3c6B0tqrt/dd99NMTExpNfrafDgwXT33Xc75CFSc92IiLZv306jR48mg8FAw4cPp7/97W8O+9X6eUJEtHPnTgLQ5XOr5vvW2NhICxYsoPj4eDIajZScnExLly6lCxcuuFRWf71v//rXvyg5OZn0ej1FR0fT3Llzqb6+Xt6vlroJRBelCWWMMcYYYw54zBJjjDHGmBMcLDHGGGOMOcHBEmOMMcaYExwsMcYYY4w5wcESY4wxxpgTHCwxxhhjjDnBwRJjjPVg586d2LBhQ18XgzHWRzhYYoz1a4IgIC8vDwDwzTffQBAEWCwWxeeXlpbiwQcfxNVXX93lfneuyRhTF21fF4Axxrozffp01NfXy8GOp+Li4lBdXY2IiAhFx//www/IyspCTk4ORowY4ZUyMMbUh4MlxtiPRkBAgEsrk4eGhqKsrMyHJWKMqQF3wzHGVOG6667D/Pnz8dhjjyEsLAzR0dFYvny5wzHl5eW49tprYTQaMXLkSOzatcth/8VdZqIoYsiQIVi7dq3DMQcPHoRGo8G3334LwLEbDwCKi4uRmpoKo9GI8ePH4+DBgw7nb9y4EWaz2WFbXl4eBEFw2LZ27VoMHToUer0el19+Od544w15HxFh+fLliI+Ph8FgQGxsLObPn+/K08UY8yIOlhhjqrFp0yYMGDAARUVFeOGFF/DHP/5RDohEUcQdd9wBvV6PoqIivPbaa1i8eHG319JoNLjnnnuwdetWh+1btmxBRkYGEhISLjmnubkZv/rVrzBy5EiUlJRg+fLl+P3vf+9yPXJzc7FgwQIsWrQIZWVl+O1vf4v7778f+fn5AIB///vfeOmll/D666+jvLwceXl5GDNmjMuPwxjzDu6GY4ypRkpKCrKzswEAw4YNwyuvvII9e/bgl7/8JXbv3o0vv/wSO3fuRGxsLADg2WefxY033tjt9bKysvCnP/0JlZWViI+PhyiKyMnJwZNPPtnl8Vu3boUoili/fj2MRiNGjRqFEydOYPbs2S7VY9WqVZg+fTrmzJkDAHjkkUfwySefYNWqVfj5z3+OyspKREdHIzMzEzqdDvHx8ZgwYYJLj8EY8x5uWWKMqUZKSorD3zExMairqwMAHDlyBHFxcXKgBADp6elOr3fllVdixIgRcutSQUEB6urqMGXKlC6PP3LkCFJSUmA0GhU/RnfXycjIcNiWkZGBI0eOAACmTJmCc+fOITk5GTNnzkRubi6sVqvLj8MY8w4OlhhjqqHT6Rz+FgQBoih6dM2srCw5WNq6dStuuOEGhIeHu309jUYDInLY1tbW5tI14uLicPToUbz66qswmUyYM2cOrr32WpevwxjzDg6WGGP9wogRI1BVVYXq6mp52yeffNLjeffeey/KyspQUlKCd955B1lZWU4f49ChQzh//ny3jxEZGYmmpiacPXtW3tY5B9OIESOwf/9+h2379+/HyJEj5b9NJhNuueUWrFmzBnv37kVhYSEOHz7cY30YY97HY5YYY/1CZmYmLrvsMkybNg0vvvgiGhsbsXTp0h7PS0xMxMSJE/HAAw/AZrPh1ltv7fbYe++9F0uXLsXMmTPx+OOP45tvvsGqVascjklLS0NgYCCeeOIJzJ8/H0VFRdi4caPDMY8++ijuuusupKamIjMzE9u3b8e2bduwe/duAPYZdTabTb7W5s2bYTKZuhx0zhjzPW5ZYoz1CxqNBrm5uTh37hwmTJiABx98ECtWrFB0blZWFkpLSzF58mSYTKZujwsKCsL27dtx+PBhpKamYunSpXj++ecdjgkLC8PmzZvxwQcfYMyYMXjzzTcvSXFw++23Y/Xq1Vi1ahVGjRqF119/HRs2bMB1110HADCbzVi3bh0yMjKQkpKC3bt3Y/v27R51DzLG3CdQ5851xhhjjDEm45YlxhhjjDEnOFhijDHGGHOCgyXGGGOMMSc4WGKMMcYYc4KDJcYYY4wxJzhYYowxxhhzgoMlxhhjjDEnOFhijDHGGHOCgyXGGGOMMSc4WGKMMcYYc4KDJcYYY4wxJzhYYowxxhhz4v+6Y8hsHN+8tQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exp3dropA_filt_df = exp3dropA_df.groupby([\"Indivíduos\"])[[\"X1\", \"X2\", \"Fitness\"]].agg([\"mean\", \"std\", \"median\"])\n",
        "exp3dropA_filt_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "690a8fe8-b8c5-4dd4-b0ed-fe3e07361370",
        "id": "dz97sUKRJj6j"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  X1                            X2                      \\\n",
              "                mean       std    median      mean       std    median   \n",
              "Indivíduos                                                               \n",
              "10.0       -0.035294  0.311119 -0.017180  0.008972  0.379689 -0.004291   \n",
              "20.0       -0.031239  0.318313 -0.001523  0.023300  0.211270  0.003508   \n",
              "30.0        0.027709  0.253181  0.000717  0.034454  0.228791  0.004273   \n",
              "40.0        0.037762  0.246514  0.001001  0.023789  0.255136  0.001137   \n",
              "50.0        0.045019  0.233893  0.000969 -0.002234  0.153028 -0.000025   \n",
              "60.0       -0.046376  0.154441 -0.002483  0.006381  0.145963 -0.001846   \n",
              "70.0       -0.019621  0.108288 -0.000203  0.002830  0.182525 -0.000041   \n",
              "80.0       -0.001402  0.140940  0.000264 -0.009957  0.105061 -0.000387   \n",
              "90.0        0.014991  0.144969  0.000230 -0.001974  0.123788  0.000293   \n",
              "\n",
              "             Fitness                      \n",
              "                mean       std    median  \n",
              "Indivíduos                                \n",
              "10.0       -0.952343  0.024750 -0.936245  \n",
              "20.0       -0.972264  0.028475 -0.989383  \n",
              "30.0       -0.976084  0.026967 -0.993172  \n",
              "40.0       -0.983761  0.025387 -0.998217  \n",
              "50.0       -0.986604  0.021465 -0.997390  \n",
              "60.0       -0.992101  0.017149 -0.998870  \n",
              "70.0       -0.995129  0.012291 -0.999530  \n",
              "80.0       -0.995451  0.011689 -0.999389  \n",
              "90.0       -0.994024  0.014437 -0.999437  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ee52349d-805f-443c-81c2-1c78871fa858\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">X1</th>\n",
              "      <th colspan=\"3\" halign=\"left\">X2</th>\n",
              "      <th colspan=\"3\" halign=\"left\">Fitness</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Indivíduos</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>-0.035294</td>\n",
              "      <td>0.311119</td>\n",
              "      <td>-0.017180</td>\n",
              "      <td>0.008972</td>\n",
              "      <td>0.379689</td>\n",
              "      <td>-0.004291</td>\n",
              "      <td>-0.952343</td>\n",
              "      <td>0.024750</td>\n",
              "      <td>-0.936245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20.0</th>\n",
              "      <td>-0.031239</td>\n",
              "      <td>0.318313</td>\n",
              "      <td>-0.001523</td>\n",
              "      <td>0.023300</td>\n",
              "      <td>0.211270</td>\n",
              "      <td>0.003508</td>\n",
              "      <td>-0.972264</td>\n",
              "      <td>0.028475</td>\n",
              "      <td>-0.989383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30.0</th>\n",
              "      <td>0.027709</td>\n",
              "      <td>0.253181</td>\n",
              "      <td>0.000717</td>\n",
              "      <td>0.034454</td>\n",
              "      <td>0.228791</td>\n",
              "      <td>0.004273</td>\n",
              "      <td>-0.976084</td>\n",
              "      <td>0.026967</td>\n",
              "      <td>-0.993172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40.0</th>\n",
              "      <td>0.037762</td>\n",
              "      <td>0.246514</td>\n",
              "      <td>0.001001</td>\n",
              "      <td>0.023789</td>\n",
              "      <td>0.255136</td>\n",
              "      <td>0.001137</td>\n",
              "      <td>-0.983761</td>\n",
              "      <td>0.025387</td>\n",
              "      <td>-0.998217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50.0</th>\n",
              "      <td>0.045019</td>\n",
              "      <td>0.233893</td>\n",
              "      <td>0.000969</td>\n",
              "      <td>-0.002234</td>\n",
              "      <td>0.153028</td>\n",
              "      <td>-0.000025</td>\n",
              "      <td>-0.986604</td>\n",
              "      <td>0.021465</td>\n",
              "      <td>-0.997390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60.0</th>\n",
              "      <td>-0.046376</td>\n",
              "      <td>0.154441</td>\n",
              "      <td>-0.002483</td>\n",
              "      <td>0.006381</td>\n",
              "      <td>0.145963</td>\n",
              "      <td>-0.001846</td>\n",
              "      <td>-0.992101</td>\n",
              "      <td>0.017149</td>\n",
              "      <td>-0.998870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70.0</th>\n",
              "      <td>-0.019621</td>\n",
              "      <td>0.108288</td>\n",
              "      <td>-0.000203</td>\n",
              "      <td>0.002830</td>\n",
              "      <td>0.182525</td>\n",
              "      <td>-0.000041</td>\n",
              "      <td>-0.995129</td>\n",
              "      <td>0.012291</td>\n",
              "      <td>-0.999530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80.0</th>\n",
              "      <td>-0.001402</td>\n",
              "      <td>0.140940</td>\n",
              "      <td>0.000264</td>\n",
              "      <td>-0.009957</td>\n",
              "      <td>0.105061</td>\n",
              "      <td>-0.000387</td>\n",
              "      <td>-0.995451</td>\n",
              "      <td>0.011689</td>\n",
              "      <td>-0.999389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90.0</th>\n",
              "      <td>0.014991</td>\n",
              "      <td>0.144969</td>\n",
              "      <td>0.000230</td>\n",
              "      <td>-0.001974</td>\n",
              "      <td>0.123788</td>\n",
              "      <td>0.000293</td>\n",
              "      <td>-0.994024</td>\n",
              "      <td>0.014437</td>\n",
              "      <td>-0.999437</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee52349d-805f-443c-81c2-1c78871fa858')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ee52349d-805f-443c-81c2-1c78871fa858 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ee52349d-805f-443c-81c2-1c78871fa858');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-e3bd368f-08f6-4d90-98e9-138781100a18\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e3bd368f-08f6-4d90-98e9-138781100a18')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-e3bd368f-08f6-4d90-98e9-138781100a18 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_5643ce12-c6d0-4bef-ad49-7070f38d9f9b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('exp3dropA_filt_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5643ce12-c6d0-4bef-ad49-7070f38d9f9b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('exp3dropA_filt_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp3dropA_filt_df",
              "summary": "{\n  \"name\": \"exp3dropA_filt_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": [\n        \"Indiv\\u00edduos\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.386127875258307,\n        \"min\": 10.0,\n        \"max\": 90.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          80.0,\n          20.0,\n          60.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.033905204064967234,\n        \"min\": -0.04637583098060701,\n        \"max\": 0.04501900721784957,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -0.001401679687903739,\n          -0.031238960846793448,\n          -0.04637583098060701\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07754332176980133,\n        \"min\": 0.10828843588713917,\n        \"max\": 0.3183126255599614,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.14094010158811468,\n          0.3183126255599614,\n          0.15444140459767033\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X1\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005803315091626109,\n        \"min\": -0.017180250740997677,\n        \"max\": 0.0010009300321106348,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.0002641330865537976,\n          -0.0015232724057418839,\n          -0.0024830030302423166\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014661733625176539,\n        \"min\": -0.009956966608965218,\n        \"max\": 0.03445390920069446,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -0.009956966608965218,\n          0.02330007430276275,\n          0.006380587352504928\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08404858020695,\n        \"min\": 0.10506050714448384,\n        \"max\": 0.37968851092704586,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.10506050714448384,\n          0.21127019538619127,\n          0.14596266313792292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"X2\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0025776013041432376,\n        \"min\": -0.004291400194738988,\n        \"max\": 0.004272881410546656,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -0.0003873384602951484,\n          0.0035075662671537333,\n          -0.0018457873447961114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014243260610962033,\n        \"min\": -0.995451241184607,\n        \"max\": -0.9523429559980654,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -0.995451241184607,\n          -0.9722639898825939,\n          -0.992101345946826\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0065280606356937845,\n        \"min\": 0.011688812737091969,\n        \"max\": 0.02847511714668835,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.011688812737091969,\n          0.02847511714668835,\n          0.017148772711001705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Fitness\",\n        \"median\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02052001062928381,\n        \"min\": -0.9995295900626164,\n        \"max\": -0.9362449994370247,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -0.9993892318244876,\n          -0.9893828940534461,\n          -0.9988696540172453\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    }
  ]
}